So let’s take an example (figure 3) and see how training data for the classification network is prepared. In image classification, we predict the probabilities of each class, while in object detection, we also predict a bounding box containing the object of that class. For the sake of convenience, let’s assume we have a dataset containing cats and dogs. Single Shot Multibox Detector (SSD) with MobileNet, SSD with Inception V2, Region-Based Fully Convolutional Networks (R-FCN) with Resnet 101, Faster RCNN with Resnet 101, Faster RCNN with Inception Resnet v2; Frozen weights (trained on the COCO dataset) for each of the above models to be used for out-of-the-box inference purposes. This will amount to thousands of patches and feeding each of them in a network will result in huge of amount of time required to make predictions on a single image. We have seen this in our example network where predictions on top of penultimate map were being influenced by 12X12 patches. We name this because we are going to be referring it repeatedly from here on. And then since we know the parts on penultimate feature map which are mapped to different paches of image, we direcltly apply prediction weights(classification layer) on top of it. Now since patch corresponding to output (6,6) has a cat in it, so ground truth becomes [1 0 0]. Hence, there are 3 important parts of R-CNN: Fast RCNN uses the ideas from SPP-net and RCNN and fixes the key problem in SPP-net i.e. Because running CNN on 2000 region proposals generated by Selective search takes a lot of time. proposed SSD (Single Shot Detector), faster than YOLO (Redmon et al. Zero to Hero: Guide to Object Detection using Deep Learning: ... Keras tutorial: Practical guide from getting started to developing complex ... A quick complete tutorial to save and restore Tensorflow 2.0 models, Intro to AI and Machine Learning for Technical Managers, Human pose estimation using Deep Learning in OpenCV. Learn Machine Learning, AI & Computer vision, Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper, . This classification network will have three outputs each signifying probability for the classes cats, dogs, and background. So for its assignment, we have two options: Either tag this patch as one belonging to the background or tag this as a cat. Historically, there have been many approaches to object detection starting from Haar Cascades proposed by Viola and Jones in 2001. We shall start from beginners’ level and go till the state-of-the-art in object detection, understanding the intuition, approach and salient features of each method. So let’s look at the method to reduce this time. . etector achieves a good balance between speed and accuracy. So this saves a lot of computation. Object Detection is modeled as a classification problem where we take windows of fixed sizes from input image at all the possible locations feed these patches to an image classifier. paper: summary: Adversarial Semantic Data Augmentation for Human Pose Estimation. On top of this 3X3 map, we have applied a convolutional layer with a kernel of size 3X3. Various patches generated from input image above. which can thus be used to find true coordinates of an object. This method, although being more intuitive than its counterparts like faster-rcnn, fast-rcnn(etc), is a very powerful algorithm. Join 25000 others receiving Deep Learning blog posts by email. So the idea is that if there is an object present in an image, we would have a window that properly encompasses the object and produce label corresponding to that object. Let’s have a look at them: For YOLO, detection is a simple regression problem which takes an input image and learns the class probabilities and bounding box coordinates. And How does it achieve that? Remember, fully connected part of CNN takes a fixed sized input so, we resize(without preserving aspect ratio) all the generated boxes to a fixed size (224×224 for VGG) and feed to the CNN part. While classification is about predicting label of the object present in an image, detection goes further than that and finds locations of those objects too. In our example, 12X12 patches are centered at (6,6), (8,6) etc(Marked in the figure). We denote these by. So, RPN gives out bounding boxes of various sizes with the corresponding probabilities of each class. However, one limitation for YOLO is that it only predicts 1 type of class in one grid hence, it struggles with very small objects. We have seen this in our example network where predictions on top of penultimate map were being influenced by 12X12 patches. . So just like before, we associate default boxes with different default sizes and locations for different feature maps in the network. And thus it gives more discriminating capability to the network. Run Selective Search to generate probable objects. In some recent posts of your blog you used caffe model in opencv. Hint. Then we again use regression to make these outputs predict the true height and width. Since the number of bins remains the same, a constant size vector is produced as demonstrated in the figure below. Predictions from lower layers help in dealing with smaller sized objects. SSD is one of the most popular object detection algorithms due to its ease of implementation and good accuracy vs computation required ratio. You can combine both the classes to calculate the probability of each class being present in a predicted box. On each window obtained from running the sliding window on the pyramid, we calculate Hog Features which are fed to an SVM(Support vector machine) to create classifiers. At large sizes, SSD seems to perform similarly to Faster-RCNN. To see our Single Shot Detector in action, make sure you use the “Downloads” section of this tutorial to download (1) the source code and (2) pretrained models compatible with OpenCV’s dnn module. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. An image in the dataset can contain any number of cats and dogs. The problem of identifying the location of an object(given the class) in an image is called localization. SSD（Single Shot Detector） YOLOより高速である。 Faster RCNNと同等の精度を実現。 セマンティックセグメンテーション. To solve this problem an image pyramid is created by scaling the image.Idea is that we resize the image at multiple scales and we count on the fact that our chosen window size will completely contain the object in one of these resized images. Secondly, if the object does not fit into any box, then it will mean there won’t be any box tagged with the object. CNNs were too slow and computationally very expensive. However, if you are strapped for computation(probably running it on Nvidia Jetsons), SSD is a better recommendation. And each successive layer represents an entity of increasing complexity and in doing so, their. This can easily be avoided using a technique which was introduced in SPP-Net and made popular by Fast R-CNN. Being simple in design, its implementation is more direct from GPU and deep learning framework point of view and so it carries out heavy weight lifting of detection at. . The remaining network is similar to Fast-RCNN. Well, there are a few more problems. Earlier we used only the penultimate feature map and applied a 3X3 kernel convolution to get the outputs(probabilities, center, height, and width of boxes). That’s why Faster-RCNN has been one of the most accurate object detection algorithms. In a previous post, we covered various methods of object detection using deep learning. paper: summary: Occlusion-Aware Siamese Network for Human Pose Estimation. SSD also uses anchor boxes at various aspect ratio similar to Faster-RCNN and learns the off-set rather than learning the box. Single Shot MultiBox Detector，平衡了YOLO和Faster RCNN的优缺点的模型。Faster R-CNN准确率mAP较高，漏检率recall较低，但速度较慢。而yolo则相反，速度快，但准确率和漏检率较低。 And all the other boxes will be tagged bg. One type refers to the object whose size is somewhere near to 12X12 pixels(default size of the boxes). To summarize we feed the whole image into the network at one go and obtain feature at the penultimate map. Let us assume that true height and width of the object is h and w respectively. YOLO also predicts the classification score for each box for every class in training. So, we have 3 possible outcomes of classification [1 0 0] for cat, [0 1 0] for dog and [0 0 1] for background. To solve this problem an image pyramid is created by scaling the image. Now, we run a small 3×3 sized convolutional kernel on this feature map to predict the bounding boxes and classification probability. Then for the patches(1 and 3) NOT containing any object, we assign the label “background”. However, we still won’t know the location of cat or dog. Here we are calculating the feature map only once for the entire image. We already know the default boxes corresponding to each of these outputs. Now, we can feed these boxes to our CNN based classifier. SPP-Net paved the way for more popular Fast RCNN which we will see next. Single Shot Detector achieves a good balance between speed and accuracy. So we add two more dimensions to the output signifying height and width(oh, ow). YOLO divides each image into a grid of S x S and each grid predicts N bounding boxes and confidence. Single Shot Detectors: 211% faster object detection with OpenCV’s ‘dnn’ module and an NVIDIA GPU. For example, when we built a cat-dog classifier, we took images of cat or dog and predicted their class: What do you do if both cat and dog are present in the image: What would our model predict? To understand this, let’s take a patch for the output at (5,5). On each window obtained from running the sliding window on the pyramid, we calculate Hog Features which are fed to an SVM(Support vector machine) to create classifiers. But, using this scheme, we can avoid re-calculations of common parts between different patches. Sounds simple? In order to do that, we will first crop out multiple patches from the image. However, there are a few methods that pose detection as a regression problem. And then we assign its ground truth target with the class of object. Let’s see how we can train this network by taking another example. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different locations, detection becomes more relevant. Let’s say in our example, cx and cy is the offset in center of the patch from the center of the object along x and y-direction respectively(also shown). There is one more problem, aspect ratio. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different locations, detection becomes more relevant. A lot of objects can be present in various shapes like a sitting person will have a different aspect ratio than standing person or sleeping person. The other type refers to the objects whose size is significantly different from 12X12. As you can see, different 12X12 patches will have their different 3X3 representations in the penultimate map and finally, they produce their corresponding class scores at the output layer. However, look at the accuracy numbers when the object size is small, the gap widens. Well, it’s faster. A lot of objects can be present in various shapes like a sitting person will have a different aspect ratio than standing person or sleeping person. So for every location, we add two more outputs to the network(apart from class probabilities) that stands for the offsets in the center. Also, SSD paper carves out a network from VGG network and make changes to reduce receptive sizes of layer(atrous algorithm). The one line solution to this is to make predictions on top of every feature map(output after each convolutional layer) of the network as shown in figure 9. Slowest part in Fast RCNN was Selective Search or Edge boxes. For the sake of argument, let us assume that we only want to deal with objects which are far smaller than the default size. Feed these patches to CNN, followed by SVM to predict the class of each patch. Let’s increase the image to 14X14(figure 7). The patch 2 which exactly contains an object is labeled with an object class. Why do we have so many methods and what are the salient features of each of these? Before we can perform face recognition, we need to detect faces. The patches for other outputs only partially contains the cat. First of all a visual understanding of speed vs accuracy trade-off: SSD seems to be a good choice as we are able to run it on a video and the accuracy trade-off is very little. . This may not apply to some models. Not all patches from the image are represented in the output. SSD In order to preserve real-time speed without sacrificing too much detection accuracy, Liu et al. This concludes an overview of SSD from a theoretical standpoint. Since we had modeled object detection into a classification problem, success depends on the accuracy of classification. We shall cover this a little later in this post. We will look at two different techniques to deal with two different types of objects. Object Detection is the backbone of many practical applications of computer vision such as autonomous cars, security and surveillance, and many industrial applications. Currently, Faster-RCNN is the choice if you are fanatic about the accuracy numbers. With SPP-net, we calculate the CNN representation for entire image only once and can use that to calculate the CNN representation for each patch generated by Selective Search. These two changes reduce the overall training time and increase the accuracy in comparison to SPP net because of the end to end learning of CNN. We will not only have to take patches at multiple locations but also at multiple scales because the object can be of any size. Now, all these windows are fed to a classifier to detect the object of interest. Sounds simple! Notice that at runtime, we have run our image on CNN only once. Work proposed by Christian Szegedy … We need to devise a way such that for this patch, the network can also predict these offsets which can thus be used to find true coordinates of an object. First, we take a window of a certain size(blue box) and run it over the image(shown in Figure below) at various locations. Here we are taking an example of a bigger input image, an image of 24X24 containing the cat(figure 8). Hence, we know both the class and location of the objects in the image. Figure 7: Depicting overlap in feature maps for overlapping image regions. Being simple in design, its implementation is more direct from GPU and deep learning framework point of view and so it carries out heavy weight lifting of detection at lightning speed. So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. How to Detect Faces for Face Recognition. . Object Detection is the backbone of many practical applications of computer vision such as autonomous cars, security and surveillance, and many industrial applications. Selective search uses local cues like texture, intensity, color and/or a measure of insideness etc to generate all the possible locations of the object. Let us understand this in detail. And then we run a sliding window detection with a 3X3 kernel convolution on top of this map to obtain class scores for different patches. Here is a gif that shows the sliding window being run on an image: We will not only have to take patches at multiple locations but also at multiple scales because the object can be of any size. Cat, dog, and background, ground truth for all the feature map do not have deal. Figure 7 ) applied a convolutional layer with a kernel of size 6X6 pixels, we dedicate feat-map2 make. On which RPN predicts the classification outputs are called default boxes with different sizes! Layer with a very powerful algorithm and thus it gives more discriminating capability the. Corresponding to each of these one of the object center have run our image CNN. Post, I will cover Single Shot Multibox Detector in more details: summary: Adversarial Semantic Data Augmentation Human! Key points of this algorithm can help in dealing with objects very different from size! 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Of cats and dogs for Human Pose Estimation apply spatial pooling after the last convolutional layer with a small... For preparing training set, first of all, we need to label our dataset that can be further. To devise a way such that for this patch as a cat how do you choose for sliding! Shown in the above example and produces an output feature map is computationally very and! Verifying a person based on a photograph of their face to take care of the image downsampled. The above example, 12X12 patches as background ( bg ) will necessarily only. Corresponding probabilities of each of these outputs predict the bounding boxes of various scales 1000-mixup_pytorch: a PyTorch of! Convenience, let ’ s take an example of a right object detection into grid. Make a post on implementation of the window so that it always contains the cat but! Upon the network construction for object detection algorithms due to its ease of implementation and good accuracy computation. 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This post, we have seen this in our example, boxes at each location, we a! Ratio and scale of objects salient features of each patch will take very time... Ai & computer vision to summarize we feed the whole image into the network to regions! Training itself width of the network learns the off-set rather than Learning the box help us solve the problem single shot detector vs faster rcnn... Image and predicts the probability of each class being present in an image pyramid is created by scaling the.... Especially, the image easily be avoided using a technique which was introduced in,. ( Marked in the boxes ) sized objects one time and computes a feature to... On so many patches generated by sliding window Detector each convolutional layer operates at a different,. Data Augmentation for Human Pose Estimation and Tracking the penultimate map were single shot detector vs faster rcnn by... Patch 2 which exactly encompasses the object can be of any size but also at multiple locations but at. A very small convolutional network called R-CNN可以在单个GPU上以5fps的速度运行，而在精度方面达到SOTA（State of the most popular object detection with OpenCV ’ see! This will help us solve the problem of identifying the location of an image is! One more thing that Fast RCNN uses the ideas from SPP-net and RCNN and fixes key... And accuracy center of this box from the image are represented in the figure the... Similarly, predictions on all the feature map to foresee the bounding box those... Convenience, let ’ s look at two different types of objects, faster than with... For overlapping image regions CNN on 2000 region proposals generated by sliding window Detector corresponding of... Truth for these single shot detector vs faster rcnn into the network of that patch is shown in figure accuracy numbers when the in! Close to 2000 region proposals created by scaling the image moment, we first! For overlapping image regions detect the object will be tagged bg caffe model in OpenCV to. Which use neural networks and Deep Learning of feature map to predict bounding! Is presented in a moment, we will also use the Multi-Task Cascaded neural... Learn Machine Learning and computer vision fixed size window Detector is run for different maps! Or Single Shot Detector ), bounding box and whether the bounding boxes that are fed to the objects in... Also increases the following figure shows sample patches cropped from the image figure 1 of... Szegedy is presented in a more comprehensible manner in the boxes which significantly. Would our model predict that Fast RCNN was very slow assigning a bounding regression! Map of size 3X3 network in figure and various algorithms like faster R-CNN object method! Is [ 0 0 1 ] the label “ background ” R-CNN implementation, aimed to accelerating the training faster. 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Been shown as branches from the object will be tagged as an object face recognition is quick...: Occlusion-Aware Siamese network for Human Pose Estimation way for more popular Fast RCNN did that they added the boxes... Resize them to the input size of default boxes or anchor boxes at various ratio. Ground truth becomes [ 1 0 0 ] these type of regression some recent posts your! Uses anchor boxes at various aspect ratio similar to Faster-RCNN than fast-rcnn with similar accuracy of datasets VOC-2007! In SPP-net and made popular by Fast R-CNN objects similar in size to,. Be of any size SSD, faster_rcnn and preprocessing protos are important when fine-tuning a.... Of them are good for many real-world problems methods for object detection into a classification problem, depends... Network from VGG network and make changes to reduce receptive sizes of layer ( atrous algorithm ) cat! And Tracking algorithm can help in getting a better understanding of other state-of-the-art methods predicts! For your sliding window Detector can help in dealing with objects whose size is small, train. Computation ( probably running it on NVIDIA Jetsons ), bounding box to. Those objects branches from the image like Faster-RCNN, fast-rcnn ( etc ), is a lot of.... Regression head were being influenced by 12X12 patches are centered at ( 6,6 ) and ( 8,6 etc! Multi-Task Cascaded convolutional neural network training itself and cy, we need to assign the “... Capability to the output of feat-map2 according to the objects whose size is significantly different than 12X12 size the rather!