Improved YOLOv8s Detection Algorithm for Remote Sensing Images

Abstract

In response to challenges posed by complex backgrounds, diverse target angles, and numerous small targets in remote sensing images, alongside the issue of high resource consumption hindering model deployment, we propose an enhanced, lightweight you only look once version 8 small (YOLOv8s) detection algorithm. Regarding network improvements, we first replace traditional horizontal boxes with rotated boxes for target detection, effectively addressing difficulties in feature extraction caused by varying target angles. Second, we design a module integrating convolutional neural networks (CNN) and Transformer components to replace specific C2f modules in the backbone network, thereby expanding the model’s receptive field and enhancing feature extraction in complex backgrounds. Finally, we introduce a feature calibration structure to mitigate potential feature mismatches during feature fusion. For model compression, we employ a lightweight channel pruning technique based on localized mean average precision (LMAP) to eliminate redundancies in the enhanced model. Although this approach results in some loss of detection accuracy, it effectively reduces the number of parameters, computational load, and model size. Additionally, we employ channel-level knowledge distillation to recover accuracy in the pruned model, further enhancing detection performance. Experimental results indicate that the enhanced algorithm achieves a 6.1% increase in mAP50 compared to YOLOv8s, while simultaneously reducing parameters, computational load, and model size by 57.7%, 28.8%, and 52.3%, respectively.

Keywords

YOLOv8s / remote sensing image / target detection / model pruning / knowledge distillation