Small Object Detection Based on Lightweight Feature Pyramid

In today's information age, consumer drones have received more and more attention in the field of consumer electronics. Consumer drones are widely used by consumers in aerial photography, follow-up photography and other scenes. There are many researches that can be carried out in the perspective of UAV, and object detection in the perspective of UAV belongs to one of them. Driven by object detection technology, drones with intelligent perception capabilities can achieve efficient and flexible data collection capabilities. However, due to the large number, large scale variation and uneven distribution of small and medium-sized targets captured by UAV, the existing algorithms have high miss detection rate and error rate for small and medium-sized targets captured by UAV. In order to be better applied to the object detection task in the view of UAV, three modules are designed in this paper. The small object enhancement module, which consists of a feature downsampling layer and a convolution-free step layer, to better cope with the detection and classification tasks of small objects and low-resolution images. The lightweight upsampling feature pyramid structure proposed at the same time has a large receptive field, through the convolution kernel forecast block and the characteristic integration block, it makes the semantics more balanced and enhances the ability of the model to locate small objects. Finally, FocalMAE Loss which reduces the negative impact of low quality samples on the gradient and helps the model converge quickly. The proposed method achieves 8.1% improvement over the conventional method. By reducing the number of parameters and maintaining lightweight functions, it exhibits improved performance in detecting small objects in UAV images.