AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning

Applied Sciences

Duo Zhang¹  Yanhua Shao¹  Hongyu Chu¹  Xingping Zhang¹  Xiaoqiang Zhang¹  Yunbo Rao² 

[1] School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China;School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China;

关键词: infrared imaging;    aerial object detection;    human detection;    prune;    YOLO;   

DOI  :  10.3390/app12073627

来源: DOAJ

【 摘 要 】

Aerial object detection acts a pivotal role in searching and tracking applications. However, the large model, limited memory, and computing power of embedded devices restrict aerial pedestrian detection algorithms’ deployment on the UAV (unmanned aerial vehicle) platform. In this paper, an innovative method of aerial infrared YOLO (AIR-YOLOv3) is proposed, which combines network pruning and the YOLOv3 method. Firstly, to achieve a more appropriate number and size of the prior boxes, the prior boxes are reclustered. Then, to accelerate the inference speed on the premise of ensuring the detection accuracy, we introduced Smooth-L1 regularization on channel scale factors, and we pruned the channels and layers with less feature information to obtain a pruned YOLOv3 model. Meanwhile, we proposed the self-built aerial infrared dataset and designed ablation experiments to perform model evaluation well. Experimental results show that the AP (average precision) of AIR-YOLOv3 is 91.5% and the model size is 10.7 MB (megabyte). Compared to the original YOLOv3, its model volume compressed by 228.7 MB, nearly 95.5 %, while the model AP decreased by only 1.7%. The calculation amount is reduced by about 2/3, and the inference speed on the airborne TX2 has been increased from 3.7 FPS (frames per second) to 8 FPS.

【 授权许可】

Unknown