PS-YOLO: a small object detector based on efficient convolution and multi-scale feature fusion

Abstract

Compared to generalized object detection, research on small object detection has been slow, mainly due to the need to learn appropriate features from limited information about small objects. This is coupled with difficulties such as information loss during the forward propagation of neural networks. In order to solve this problem, this paper proposes an object detector named PS-YOLO with a model: (1) Reconstructs the C2f module to reduce the weakening or loss of small object features during the deep superposition of the backbone network. (2) Optimizes the neck feature fusion using the PD module, which fuses features at different levels and sizes to improve the model’s feature fusion capability at multiple scales. (3) Design the multi-channel aggregate receptive field module (MCARF) for downsampling to extend the image receptive field and recognize more local information. The experimental results of this method on three public datasets show that the algorithm achieves satisfactory accuracy, prediction, and recall.