Abstract
AbstractDeep learning is an important research field of machine learning. In recent years, many breakthroughs have been made in the field of target detection, which has been applied to specific target detection tasks. This paper first introduces the representative traditional detection methods and discusses their limitations, and then introduces in detail the target detection models based on candidate regions and regression. At the same time, their advantages are summarized and compared. Through the comprehensive test of PASCAL VOC2007 and VOC 2012 test data, the results show that the DSSD detection method is the best, and the YOLOv3 detection method is the best for vehicle detection. In addition, the self-produced data set of field construction vehicles was used to detect Yolov3 and test the detection accuracy. The results showed that the detection accuracy was above 0.9. Yolov3 can be well used for vehicle detection. Finally, the detection methods based on deep learning are summarized.KeywordsDeep learningTarget detectionRegion proposal modelRegression mode
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