Top object detection algorithms in deep neural networks
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This document discusses object detection using deep neural networks. It describes different types of neural networks including convolutional neural networks (CNNs), which are well-suited for object detection tasks. CNNs use techniques like parameter sharing and sparse connections to recognize visual patterns. Popular object detection algorithms that use CNNs are R-CNN, which proposes regions and classifies them, and YOLO (You Only Look Once), which frames detection as a single regression problem from image to bounding boxes. While very fast, YOLO struggles with small or grouped objects and unusual aspect ratios.
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Top object detection algorithms in deep neural networks
- 1. 1 OBJECT DETECTION USING DEEP NEURAL NETWORK
- 2. 2 What is deep neural networks? A technology built to simulate the activity of the human brain. Has several layers including input & output layers. Each layer performs specific types of functions. Uses in image recognition, verification, classification, object detection, real-time object detection, face recognition etc.
- 3. 3 TYPES OF NEURAL NETWORKS Artificial Neural Network (ANN) Deep Residual Network (Deep ResNet) Recurrent Neural Network (RNN) Convolutional Neural Network (CNN)
- 4. 4 CONVOLUTIONAL NEURAL NETWORK(CNN) One of the most popular neural networks. Most commonly used in images recognition, images classifications. Object detections, recognition faces, computer vision etc. Types of CNN: LeNet-5 AlexNet VGG-16 Inception Network GoogleNet etc.
- 5. 5 CNN IN OBJECT DETECTION Very good for recognizing patterns such as edges (vertical/horizontal), shapes, colours, and textures. Most popular neural network for object detection. Why CNN?: Parameter sharing Sparsity of connections
- 6. 6 OBJECT DETECTION ALGORITHMS Some popular object detection algorithms are: Deformable parts models(DPM) R-CNN(Region-CNN) YOLO(You Only Look Once)
- 7. 7 Deformable parts models(DPM) Use a sliding window approach to object detection DPM uses a disjoint pipeline to - extract static features classify regions predict bounding boxes for high scoring regions, etc.
- 8. 8 Fig: Deformable parts models
- 9. 9 R-CNN(Region-CNN) One of the most popular object detection algorithm Uses region proposals in-stead of sliding windows First generate potential bounding boxes in an image Run a classifier on these proposed boxes. Post-processing is used to refine the bounding boxes, eliminate duplicate detections, and rescore the boxes based on other objects in the scene.
- 10. 10
- 11. 11 LIMITATIONS OF R-CNN Complex pipelines are slow Hard to optimize Individual component must be trained separately
- 12. 12 YOLO(YOU ONLY LOOK ONCE) According to the paper “You Only Look Once:Unified, Real-Time Object Detection” - by Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi: Reframe object detection as a single regression problem Straight from image pixels to bounding box coordinates and class probabilities
- 13. 13 METHODOLOGY A single convNet simultaneously predicts multiple bounding boxes Calculate class probabilities for those boxes Divides the input image into an S × S grid. center of an object is responsible for detecting that object. Each grid cell predicts B bounding boxes and confidence scores for those boxes. Confidence scores reflect how confident the model is that the box contains an object
- 15. 15 METHODOLOGY How accurate it thinks the box is that it predicts Confidence can be defined as Pr(Object) ∗ IOU truth Each bounding box consists of 5 predictions: x, y, w, h,and confidence.
- 16. 16 Fig: YOLO convolution layers
- 17. 17 WHY YOLO? YOLO is extremely fast It doesn’t need a complex pipeline YOLO achieves more than twice the mean average precision of other real-time systems. YOLO sees the entire image during training and test time YOLO makes less than half the number of background errors compared to Fast R-CNN. YOLO outperforms top detection methods like DPM and R-CNN by a wide margin
- 18. 18 LIMITATIONS YOLO imposes strong spatial constraints on bounding box predictions. Model struggles with small objects that appear in groups, such as flocks of birds. It struggles to generalize to objects in new or unusual aspect ratios or configurations. Main source of error is incorrect localizations.