Object detection of Gaussian-YOLO v3 implanting attention and feature intertwine modules
LIU Dan1, WU Yajuan1, LUO Nanchao2, ZHENG Bochuan3
1. School of Computer Science, China West Normal University, Nanchong Sichuan 637002, China; 2. School of Computer Science and Technology, Aba Teachers University, Aba Sichuan 623002, China; 3. School of Mathematics and Information, China West Normal University, Nanchong Sichuan 637002, China
Abstract：Wrong object detection may lead to serious accidents, so high-precision object detection is very important in autonomous driving. An object detection method of Gaussian-YOLO v3 combining attention and feature intertwine module was proposed, in which several specific feature maps were mainly improved. First, the attention module was added to the feature map to learn the weight of each channel autonomously, enhancing the key features and suppressing the redundant features, so as to enhance the network ability to distinguish foreground object and background. Second, at the same time, different channels of the feature map were intertwined to obtain more representative features. Finally, the features obtained by the attention and feature intertwine modules were fused to form a new feature map. Experimental results show that the proposed method achieves mAP (mean Average Precision) of 20.81% and F1 score of 18.17% on BDD100K dataset, and has the false alarm rate decreased by 3.5 percentage points, reducing the false alarm rate effectively. It can be seen that the detection performance of the proposed method is better than those of YOLO v3 and Gaussian-YOLO v3.
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2020, Vol. 40 
