关键词: 目标检测, 机器人, 网络结构, Tiny-YOLOV3, 损失函数

Abstract:

Tiny-YOLOV3 is a common detection algorithm in the field of object detection. Compared with YOLOV3, tiny has the advantages of simpler neural network layer, less computation and lower hardware configuration requirements, so it can ensure the real-time detection. However, due to the lack of network layer, the accuracy of tiny detection is also low. In order to improve the detection accuracy of Tiny-YOLOV3, this paper improves the network structure of Tiny-YOLOV3 by adjusting the loss structure layer of the detection network architecture, and uses the correlation coefficient matrix of the convolution layer and the characteristic graph to represent the distribution of the characteristic graph. In addition, this paper also designs the loss function to optimize the distribution of the loss feature layer and enhances the expression ability of the network features. Finally, combining with the NAO robot, the object detection position of the image is transformed into the robot coordinate system position through the trigonometric function positioning. Based on 4,000 self-made image data set training and testing, this paper designs object detection and grab experiment on the NAO robot under various scene. For 50 times experiments with a detection speed at 35 frames per second（same as the original）, the optimized model improves the map value by 4.08%, the confidence level by 20%, and the touch success rate by 10%. The results show that it meets the requirement of real time object detection and improves the detection accuracy at the same time, which will help a lot in real-time application scenarios such as robot sorting, picking, monitoring, service, etc.

Key words: object detection, robot, network structure, Tiny-YOLOV3, loss function