Engineering vehicle detection in aerial images with recursive feature fusion and parallel scaling

Aiming at the problems of poor detection accuracy caused by complex and changeable backgrounds, small objects and large scale changes in UAV aerial photography transmission corridor images, the detection method of recursive fusion and parallel scaling for engineering vehicles based on RetinaNet is proposed in this paper. This method is more suitable for detecting engineering vehicles in complex backgrounds. Firstly, the C2 layer is added as the base layer, which is used to generate feature pyramids together with the original backbone output layers to avoid small object features being highly compressed. Secondly, the original feature pyramid structure is adjusted, and recursive structure with feedback connections is used for feature extraction to enhance the characterization ability. Moreover, a novel and lightweight feature fusion strategy is designed to reconstruct the feature pyramid and makes full use of contextual information to improve the object detection capability in complex backgrounds. Finally, the parallel feature scaling branch is constructed with multiple deconvolution blocks and average pooling layers based on the C5 layer of the backbone to further increase the resolution of the feature maps and improve the detection accuracy of small objects. Experiments are carried out on the engineering vehicle APEV dataset constructed in this paper and the public Pascal VOC dataset, respectively. The experimental results show that the detection accuracy of the proposed method on the APEV dataset and the VOC dataset is 4.9% and 2.7% higher than that of the original RetinaNet network on the premise of meeting the requirements of engineering applications, respectively, Further, the proposed method also has higher detection accuracy compared with Faster R-CNN, SSD, YOLOv3, YOLOv5, LSN, S-RetinaNet and other methods.