RLRD-YOLO: An Improved YOLOv8 Algorithm for Small Object Detection from an Unmanned Aerial Vehicle (UAV) Perspective

In Unmanned Aerial Vehicle (UAV) target detection tasks, issues such as missing and erroneous detections frequently occur owing to the small size of the targets and the complexity of the image background. To improve these issues, an improved target detection algorithm named RLRD-YOLO, based on You Only Look Once version 8 (YOLOv8), is proposed. First, the backbone network initially integrates the Receptive Field Attention Convolution (RFCBAMConv) Module, which combines the Convolutional Block Attention Module (CBAM) and Receptive Field Attention Convolution (RFAConv). This integration improves the issue of shared attention weights in receptive field features. It also combines attention mechanisms across both channel and spatial dimensions, enhancing the capability of feature extraction. Subsequently, Large-Scale Kernel Attention (LSKA) is integrated to further optimize the Spatial Pyramid Pooling Fast (SPPF) layer. This enhancement employs a large-scale convolutional kernel to improve the capture of intricate small target features and minimize background interference. To enhance feature fusion and effectively integrate low-level details with high-level semantic information, the Reparameterized Generalized Feature Pyramid Network (RepGFPN) replaces the original architecture in the neck network. Additionally, a small-target detection layer is added to enhance the model’s ability to perceive small targets. Finally, the detecting head is replaced with the Dynamic Head, designed to improve the localization accuracy of small targets in complex scenarios by optimizing for Scale Awareness, Spatial Awareness, and Task Awareness. The experimental results showed that RLRD-YOLO outperformed YOLOv8 on the VisDrone2019 dataset, achieving improvements of 12.2% in mAP@0.5 and 8.4% in mAP@0.5:0.95. It also surpassed other widely used object detection methods. Furthermore, experimental results on the HIT-HAV dataset demonstrate that RLRD-YOLO sustains excellent precision in infrared UAV imagery, validating its generalizability across diverse scenarios. Finally, RLRD-YOLO was deployed and validated on the typical airborne platform, Jetson Nano, providing reliable technical support for the improvement of detection algorithms in aerial scenarios and their practical applications.