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

Lu, Cheng-Kai and Shen, Jun-Yu and Lin, Cheng-Hung and Lien, Chung-Yueh and Yen, Ding Su (2025) Efficient Embedded System for Small Object Detection: A Case Study on Floating Debris in Environmental Monitoring. IEEE Embedded Systems Letters. ISSN 19430663

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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 2.8W power consumption, making it ideal for large-scale environmental monitoring. © 2009-2012 IEEE.

Item Type: Article
Impact Factor: Cited by: 0
Uncontrolled Keywords: Aquatic environments; Case-studies; Detection framework; Ecological risks; Embedded-system; Environmental Monitoring; Floating debris; Objects detection; Small object detection; YOLOv4; Environmental monitoring
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 08 Jul 2025 16:28
Last Modified: 08 Jul 2025 16:28
URI: http://scholars.utp.edu.my/id/eprint/38947

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