Real-Time Localisation of Dorsal Anatomical Regions on Dairy Cows in Commercial Rotary Parlours from RGB-D
Vineet Prasad, Shiron Melvin, Sheikh Azid, Hai Wang
Automated monitoring of dairy cows increasingly measures one part of the body, such as the rump for temperature or the back for posture, yet the region itself is usually placed by hand. Existing systems either score the cow as a whole or detect sparse keypoints, often in controlled walkways, and neither yields the named region masks that such measurements need on a crowded commercial rotary platform. We present a perception front end that finds the target cow, ten bony landmarks and four named dorsal regions (withers, back, rump and tail-head) in one forward pass over a whole RGB-D frame. Colour and depth encoders are fused late and share one decoder with three heads. On an anatomical region dataset built on public rotary-parlour RGB-D images (538 annotated frames, cow-disjoint test set), the network (ConvNeXt-T backbone) reaches PCK@10 0.905 for landmarks and region IoU 0.70 to 0.91, and runs end to end in 26.4 ms per frame on a T4 GPU, against an 11 fps camera. Removing the region head costs 0.15 PCK@10 (ResNet-18), but a control shows that it mainly adds training signal: wider landmark heatmap targets recover the gain without it. Agreement between the two heads flags unreliable frames without labels. On our own rig at Western Australia's largest dairy farm, the ConvNeXt-T model transfers with ten labelled frames (PCK@10 0.85), and its regions are carried through depth into a radiometric thermal camera, giving one temperature per region and cow that is stable within a pass.
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