An improved small object detection method based on Yolo V3

In this paper, an improved algorithm based on Yolo V3 is proposed, which can effectively improve the accuracy of small target detection. First of all, the feature map acquisition network is improved. The image double-segmentation and bilinear upsampling network are used to replace the 2-step downsampling convolution network in the original network architecture, and the feature values of large and small objects are amplified. Secondly, a size recognition module is added to the input image to reduce the loss of morpheme features caused by no-feature value filling and enhance the recognition ability of small objects. Thirdly, in order to avoid the gradient fading of the network, the residual network element of the output network layer is added to enhance the feature channel of small object detection. Compared with Yolo V3, our algorithm improves the detection accuracy of small objects from 82.4 to 88.5%, the recall rate from 84.6 to 91.3%, and the average accuracy from 95.5 to 97.3%, respectively.

Automated summary

This paper proposes an improved algorithm based on Yolo V3 to enhance small target detection accuracy through three optimizations: improving the feature map acquisition network, adding a size recognition module, and enhancing feature channel. The new algorithm improves the detection accuracy, recall rate, and average accuracy of small objects compared to Yolo V3.