Aiming at the problems that the existing target detection framework based on big data and depth learning has poor recognition effect on low-resolution small targets in high-resolution complex large-field scenes, and the accuracy and real-time performance of multi-target detection are difficult to balance, this paper improved the SSD based on depth learning, and proposed an improved multi-target detection framework DRZ(dynamic region zoom-in) -SSD, which was dedicated to multi-target detection in complex large traffic scenes. It carried out the detection in a coarse-to-fine strategy. It trained a low-resolution coarse detector and a high-resolution fine detector respectively, downsampled the high-resolution image to obtain a low-resolution version, and designed a dynamic region zoom-in network based on enhanced learning. It dynamically enlarged the low-resolution small target region to a high-resolution and used the fine detector to carry out detection and identification, and detected the remaining image region by using the coarse detector, so that the detection and identification accuracy of the small target and the operation efficiency were obviously improved. It adopted fuzzy threshold method to adjust the adaptive threshold strategy to avoid adapting to the data set and improved the decision-making ability of the model and significantly reduced the detection missed alarm rate and false alarm rate. Experiments show that the improved DRZ-SSD can achieve good results when dealing with weak targets, multi-targets, cluttered background, occlusion and other difficult detection situations. Through testing on the specified data set, compared with other target detection frameworks based on deep learning, the average accuracy rate of various types of target recognition has increased by 4%~15%, the average accuracy rate has increased by 9%~16%, the multi-target detection rate has increased by 13%~34%, and the detection and recognition rate has reached 38 fps, realizing the balance between the accuracy of the algorithm and the running rate.

[2019-11-05] Printed Article /doc/2018.05.0343/v2