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17 October 2019 Real-time object detection algorithm based on improved YOLOv3
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Object detection is a challenging computer vision problem with numerous practical applications. Due to low accuracy and slow detection speed in object detection, we propose a real-time object detection algorithm based on YOLOv3. First, to solve the problem that features are likely to be lost in the feature extraction process of YOLOv3, a DB-Darknet-53 feature extraction network embedded in inception structure is designed, which effectively reduces the loss of features. Second, the detection network of YOLOv3 and the reuse of deep features in multiscale detection network are improved. Finally, the numbers and sizes of anchor boxes are selected by K-means clustering analysis, and the detection model is obtained by means of multiscale training. The improved algorithm has a mean average precision of 0.835 on the PASCAL VOC data set and a detection speed of 35.8  f  /  s, which is better than YOLOv3.

© 2019 SPIE and IS&T 1017-9909/2019/$28.00 © 2019 SPIE and IS&T
Xiuling Zhang, Xiaopeng Dong, Qijun Wei, and Kaixuan Zhou "Real-time object detection algorithm based on improved YOLOv3," Journal of Electronic Imaging 28(5), 053022 (17 October 2019).
Received: 26 May 2019; Accepted: 25 September 2019; Published: 17 October 2019


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