Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n

The water surface environment is highly complex, and floating objects in aerial images often occupy a minimal proportion, leading to significantly reduced feature representation. These challenges pose substantial difficulties for current research on the detection and classification of water surface floating objects. To address the aforementioned challenges, we proposed an improved YOLOv8-HSH algorithm based on YOLOv8n. The proposed algorithm introduces several key enhancements: (1) an enhanced HorBlock module to facilitate multi-gradient and multi-scale superposition, thereby intensifying critical floating object characteristics; (2) an optimized CBAM attention mechanism to mitigate background noise interference and substantially elevate detection accuracy; (3) the incorporation of a minor target recognition layer to augment the model's capacity to discern floating objects of differing dimensions across various environments; and (4) the implementation of the WIoU loss function to enhance the model's convergence rate and regression accuracy. Experimental results indicate that the proposed strategy yields a significant enhancement, with mAP50 and mAP50-95 increasing by 11.7% and 12.4%, respectively, while the miss rate decreases by 11%. The F1 score has increased by 11%, and the average accuracy for each category of floating objects has enhanced by a minimum of 5.6%. These improvements not only significantly enhanced the model's detection accuracy and robustness in complex scenarios but also provided new solutions for research in aerial image processing and related environmental monitoring fields.