Transmission Line Bird Species Detection and Identification Based on Double Data Enhancement and Improvement of YOLOv8s

To address the challenge of bird species detection on transmission lines, this paper proposes a detection method based on dual data enhancement and an improved YOLOv8s model. The method aims to improve the accuracy of identifying small- and medium-sized targets in bird detection scenes on transmission lines, while also accounting for the impact of changing weather conditions. To address these issues, a dual data enhancement strategy is introduced. The model’s generalization ability in outdoor environments is enhanced by simulating various weather conditions, including sunny, cloudy, and foggy days, as well as halo effects. Additionally, an improved Mosaic augmentation technique is proposed, which incorporates target density calculation and adaptive scale stitching. Within the improved YOLOv8s architecture, the CBAM attention mechanism is embedded in the Backbone network, and BiFPN replaces the original Neck module to facilitate bidirectional feature extraction and fusion. Experimental results demonstrate that the proposed method achieves high detection accuracy for all bird species, with an average precision rate of 94.2%, a recall rate of 89.7%, and an mAP@50 of 94.2%. The model also maintains high inference speed, demonstrating potential for real-time detection requirements. Ablation and comparative experiments validate the effectiveness of the proposed model, confirming its suitability for edge deployment and its potential as an effective solution for bird species detection and identification on transmission lines.