IECA-YOLOv7: A Lightweight Model with Enhanced Attention and Loss for Aerial Wildlife Detection

Grassland ecosystems are vital for global biodiversity, yet traditional wildlife monitoring methods are often labor-intensive and costly. Although drone-based aerial surveys provide a scalable alternative, they face significant challenges such as detecting extremely small targets, handling complex backgrounds, and operating under strict computational constraints. To address these issues, this study proposes IECA-YOLOv7, a lightweight detection model that incorporates three key innovations: an Improved Efficient Channel Attention (IECA) module for enhanced feature representation, a content-aware CARAFE upsampling operator for improved detail recovery, and a Normalized Wasserstein Distance (NWD) loss function for robust small-target localization. Evaluated on a dedicated grassland wildlife dataset (GWAID), the model achieves a mAP@0.5 of 86.6% and a mAP@0.5:0.95 of 47.2%, outperforming the baseline YOLOv7-tiny by 2.9% in Precision and 1.8% in Recall. Furthermore, it surpasses non-YOLO architectures such as RetinaNet, EfficientDet-D0, and DETR by significant margins, demonstrating superior performance in small-object detection under complex conditions. Cross-dataset validation on VisDrone, CARPK, and DOTA demonstrates a strong generalization capability. With a model size under 5 MB, IECA-YOLOv7 effectively balances accuracy and efficiency, offering a practical solution for real-time wildlife monitoring via drones under challenging environmental constraints such as variable lighting, occlusion, and limited computational resources, thereby supporting broader conservation efforts.