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A Drone System With Vison for Fishing Activity Detection
Abstract
Illegal, unreported, and unregulated (IUU) fishing is a major threat to marine ecosystems. The local economies also affected by IUU. Traditional methods of monitoring fishing activity are often costly, labor-intensive, and only cover limited areas. To address these limitations, this study introduces a compact, low-cost aerial surveillance system that detects fishing boats and fishing gear using drone-mounted vision system. The system consists of a Raspberry Pi 4 (8GB ram version), coupled with a high-resolution Pi Camera and integrated with a LinkIt ONE board for GPS positioning and GPRS-based communication provider. A lightweight object detection model (YOLOv8n) is deployed on the Raspberry Pi to identify boats and fishing gear from aerial video footage. Each detection is geo-tagged using real-time GPS data and transmitted to a remote server/cloud. The LinkIt ONE's GPRS (cellular) module provides data transmission connectivity. It also supplies GPS data to the system for accurate positioning. This system is designed for efficiency and runs on low power. The computational overhead of the system is very low. It supports near-real-time detection at about 3 frames per second during daylight. The solution is suitable for coastal areas and protected marine areas. It can be used in inland waters (rivers or large fisheries) if necessary. The system supports both enforcement agencies and fisheries conservation programs. The vision-based system can effectively detect fishing activity. Overall experimental results show the reliable performance of the system. The overall cost is much lower than traditional aerial surveillance methods.
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