Waterbird image recognition using lightweight deep learning in wetland environment

Wetland waterbirds serve as key ecological indicators for assessing habitat quality and biodiversity. Accurate identification of waterbird species is a cornerstone of long-term ecological monitoring. The resulting data are critical for assessing wetland ecosystem health and biodiversity. However, prevailing recognition approaches often prioritize detection accuracy at the expense of computational efficiency. They are also hindered by complex background heterogeneity and interspecies visual similarity. These limitations hinder the scalability and practical deployment of such methods for on-site ecological monitoring within wetland ecosystems. To address these challenges, this study proposes an optimized end-to-end framework, ShuffleNetV2-iRMB-ShapeIoU-YOLO (SIS-YOLO), designed for robust recognition of wetland waterbirds in complex environments. Specifically, the proposed framework integrates ShuffleNetV2 with inverted Residual Mobile Blocks (iRMB) to improve computational efficiency while maintaining robust feature representation. This design further enables deployment on resource-constrained mobile and embedded platforms. Additionally, ShapeIoU, a refined bounding box similarity metric, is introduced to jointly optimize overlap and shape consistency, effectively mitigating misclassification among visually similar species. Experimental results on the IC-Beijing dataset show that SIS-YOLO achieves 91.1% precision and 79.1% mAP@0.5:0.95 with only 2.9 million parameters. Compared with the lightweight baseline YOLOv8n, it improves precision by 2% and mAP@0.5:0.95 by 1.2%, while requiring fewer parameters and offering higher computational efficiency.