Physics-aware vision instrumentation for stingless bee counting at hive entrance using hybrid edge-cloud object detection Mohd Amri Md Yunus, Lari Andres Sanjaya, Celestine Hiu Shun Yi, Shafishuhaza Sahlan, Agus Setyo Budi
Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia
Abstract
Stingless bee entrance monitoring requires a non-invasive tool to measure colony traffic without disrupting foraging. A hybrid edge-cloud object identification system and physics-aware vision instrumentation framework are used to track stingless bees in this paper. The system uses a Raspberry Pi 4 edge node, Sony IMX296 Global Shutter camera, You Only Look Once (YOLO)-based detection, and Observation-Centric Simple Online and Realtime Tracking (OC-SORT) tracking. Because entry activity implies foraging intensity, the traffic count can be a non-invasive proxy for colony health and yield. The edge-side YOLO11 and OC-SORT tracking pipeline found trajectory fragmentation during high-speed ingress. A frame interval of 0.0667 s was achieved using a 15 FPS edge processing rate. Inter-frame displacement may approach 0.333 m for bee motion exceeding
5 ms-1, producing missed detections and identity switching. Thus, a sampling-based tracking failure situation happens when a bee travels more than the tracker^s maximum association distance between two processed frames. This illustrates that frame rate, bee velocity, field-of-view scale, detector recall, tracker association tolerance, and biologically meaningful bee counting interpretation all affect bee tracking reliability.
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