The existing pre-defined coverage path strategies in unmanned aerial vehicle (UAV)-based vertical photogrammetry are inadequate for the monitoring and surveying of spotted seal populations with dynamic group distribution characteristics. This paper presents a novel real-time, perception-driven adaptive coverage path planning (CPP) approach utilizing UAV edge computing for intelligent recognition. The proposed method integrates onboard target detection, multi-object tracking (MOT), and visual positioning during UAV patrol operations to achieve real-time perception of individual locations within mobile populations. To mitigate false detections and re-detections, this study proposes a Spatial Noise Filtering (SNF) method for UAV-based detection points. A three-level noise removal scheme is designed, consisting of a distance threshold, low-density point filtering within core regions, and global low-density filtering, enabling systematic suppression of spatial noise generated during detection. On this basis, the SNF method is integrated with a Genetic Algorithm (GA) to achieve dynamic optimization of the UAV coverage path. Experimental results show that the adopted YOLOv11 model achieves a precision of 0.9. The multi-object tracking (MOT) technique based on UAV video streams reduces redundant positioning by 61% (from 389 detection points to 150 tracking points), and the average visual positioning error is 4.9 m at flight altitudes below 75 m. The proposed SNF method effectively removes most invalid coverage regions, significantly improving the spatial reliability of the detection results. Furthermore, the GA-based optimization of the UAV coverage path enables adaptation to the uncertain spatial distribution of spotted seal populations, meeting the requirements for dynamic population surveying and real-time monitoring.
Chen et al. (Thu,) studied this question.