1. Changes and comparison of detection methods for target learning
"Object detection" is a current research hotspot in the field of computer vision and machine learning. From the wisdom of the cold weapon era such as Viola-Jones Detector and DPM to the violent aesthetics of GPUs nurtured by deep learning soil such as RCNN and YOLO today, the development of the entire target detection can be described as a condensed history in the field of computer vision. The entire development process of target detection has been summarized in the figure below: 
It can be seen that before 2012, in the field of target detection, traditional manual feature detection algorithms were still the main ones. However, with the rise of convolutional neural network (CNN) in 2012, target detection began to be violent under deep learning. aesthetics. Under deep learning, the effect of target detection is much better than that of traditional manual features. To this day, detection algorithms based on deep learning are still the mainstream of target detection.
2. Basic process of deep learning target detection algorithm
Process one:
Given a picture to be detected, use this picture as the input of the detection algorithm, then use the sliding window method to extract candidate boxes from the picture, and then extract features from the images in each candidate box (feature extraction is mainly based on Extraction method introduced in the previous pre-knowledge), and use the classifier to determine the feature classification, and obtain a series of candidate frames for the current detection target. These candidate frames may overlap. In this case, the non-maximum suppression algorithm NMS is used After merging or filtering the candidate frames, the final candidate frame is the final detection target, that is, the output result.
Process 2:
Given a picture as input, the feature extraction + target frame regression method is used to extract the target area. Finally, NMS is also used to merge the candidate frames, and finally the target output result is obtained.
Notice:
- Process 1: Applicable to traditional target detection methods and target detection methods based on deep learning
- Process 2: Suitable for target detection methods based on deep learning
3. Traditional target detection algorithm VS deep learning target detection algorithm
| Traditional target detection algorithm | Deep learning target detection algorithm |
| Manually design features | Deep network learning features |
| sliding window | Proposal or direct return |
| traditional classifier | deep network |
| multiple steps | end-to-end |
| Poor accuracy and real-time performance | High accuracy and good real-time performance |
