Efficient Embedded System for Small Object Detection: A Case Study on Floating Debris in Environmental Monitoring

Floating debris in aquatic environments poses ecological risks, necessitating prompt detection to prevent its spread into oceans, where recovery is challenging. This letter introduces an optimized YOLOv4-based detection framework tailored for small-scale floating debris on embedded systems, specifically for the Raspberry Pi 4 with integrated camera modules. Key innovations include a modified REGP pooling layer, enhanced spatial pyramid pooling (SPP), and reduced detection heads, boosting mean Average Precision (mAP) by 7.91% on the FloW dataset while reducing parameters by 26.35% compared to the baseline model. These improvements enhance computational efficiency, achieving real-time performance at 15 fps with 2.8 W power consumption, making it ideal for large-scale environmental monitoring.