MSAL-YOLO: a YOLOv8-based detector for small and densely distributed object detection in UAV aerial imagery
Year: 2026
Type: article
Abstract: Object detection in UAV aerial imagery is challenging because of small object scales, dense distributions, occlusions, and complex backgrounds. To address these issues, we propose MSAL-YOLO, an improved YOLOv8-based detector for aerial object detection. Specifically, a Spatial–Channel Mixed Convolution (SCMConv) module is introduced to enhance local feature extraction by combining standard and dilated convolutions. A Multi-Branch Enhanced Coordinate Attention (MBECA) mechanism is further designed to strengthen spatial dependency modeling and contextual interaction. In addition, a Region-Adaptive Wise-IoU (RA-WIoU) loss is developed to alleviate scale imbalance by increasing the contribution of small objects during training. Experiments on the VisDrone2019 dataset show that MSAL-YOLO improves [email protected] and [email protected]:0.95 from 30.0% and 17.0% to 35.7% and 20.5%, respectively, while maintaining competitive model complexity. Cross-dataset experiments on DOTAv1 further demonstrate the robustness and generalization capability of the proposed method. These results indicate that MSAL-YOLO provides an effective solution for UAV aerial object detection in complex scenes.
Source: Scientific Reports
Authors: Fei Ding, Xiufu Du, Haining Zhang, Haibin Liu, Liguo Han
Institution: Fuyang Normal University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7220856364