A novel algorithm for small object detection based on YOLOv4.

ABSTRACT: Small object detection is one of the difficulties in the development of computer vision, especially in the case of complex image backgrounds, and the accuracy of small object detection still needs to be improved. In this article, we present a small object detection network based on YOLOv4, which solves some obstacles that hinder the performance of traditional methods in small object detection tasks in complex road environments, such as few effective features, the influence of image noise, and occlusion by large objects, and improves the detection of small objects in complex background situations such as drone aerial survey images. The improved network architecture reduces the computation and GPU memory consumption of the network by including the cross-stage partial network (CSPNet) structu

SUBMITTER: Wei J 

PROVIDER: S-EPMC10280595 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature