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Health surveillance depends fundamentally on the reliable tracking of individual calves. Yet, prevailing multi-object tracking frameworks are prone to recurrent identification anomalies, particularly ID switches arising under occluded conditions. To overcome this limitation, we introduce YSD-BPTrack in this work, a novel multi-object tracking framework tailored for calves within occlusion-prone farm environments. The architecture is divided into two principal stages: detection and tracking. For the detection stage, DCNv4 is embedded into the YOLOv8s model to learn spatial deformations caused by occlusion, thereby strengthening detection robustness under such conditions. Meanwhile, the Star operation borrowed from StarNet is adopted to deliver high detection capability while keeping computational demands low. In the tracking stage, we first devise an original rematching mechanism (Rematching module) alongside a redesigned trajectory removal policy (Trajectory removal module). The Rematching module exploits expanded trajectory prediction boxes to re-establish correspondences with detection boxes during occlusion events, thereby cutting down on ID switch errors. The Trajectory Removal module, in turn, flexibly governs the timeout period for lost trajectories, minimizing the risk of premature trajectory deletion. Concretely, the framework we propose sets a new HOTA (Higher Order Tracking Accuracy) score of 91.6%, leading competing approaches in both tracking quality and runtime performance. Experimental evidence further demonstrates the advantage of YSD-BPTrack, with HOTA climbing by 17.6%, MOTA (Multiple Object Tracking Accuracy) by 13.9%, MOTP (Multiple Object Tracking Precision) by 1.8%, IDF1 (Identification F1 Score) by 15.4%, accompanied by reductions of 49.1% in parameters, 88.9% in IDSw (Identification Switches), and 39.2% in computational overhead relative to the other frameworks. Collectively, the presented multi-object tracking framework carries strong potential to redefine calf tracking practice.

Grain buyers reject canola kernels that have been heated or remain immature due to excessive drying or frost exposure, as these defects reduce both oil extraction rates and commercial value. The current grading protocol relies on visually inspecting each seed's endosperm hue and manually tallying the damaged specimens. Given that this method is tedious, labour-intensive, and prone to human error, the present work proposes an automated grading pipeline based on object detection, multi-object tracking, and enumeration. The detection phase was accomplished using a refined YOLOv7 architecture (YOLOv7_ours), which was adapted to bolster its small-object recognition accuracy by embedding two convolutional block attention modules into the neck section, while simultaneously lowering its computational demand (cost) and footprint by swapping conventional convolutional layers for ghost layers across all Efficient Layer Aggregation Networks modules and within the Spatial Pyramid Pooling Cross Stage Partial module native to YOLOv7. The trained network parameters were then passed to the ByteTrack multiple-object tracker, enabling frame-by-frame tracking of detections throughout a video sequence. The distinct identifiers generated by the tracker for each target of interest were subsequently used to tabulate defect counts using a line-crossing procedure. Following training, the YOLOv7_ours model delivered a mean average precision ([email protected]) that was 1.02% superior, alongside a 32.1% reduction in cost and a 37.1% reduction in size relative to the baseline YOLOv7 model. Assessed on a test video, the full pipeline achieved a multi-object tracking accuracy of 84.8% and a counting accuracy of 93.9%. This three-component framework is ready for integration into edge devices, enabling grain buyers to precisely grade canola kernels in real time.