A low-stress dual-modal imaging system and dead chicken detection method for commercial layer farms

Abstract

Conventional methods for detecting dead chickens in commercial poultry farming rely heavily on labor-intensive manual inspections, which are prone to inefficiency, biosecurity risks, and human error. While sensor-based and computer vision techniques have improved automated detection, single-modality methods still face significant limitations: visible-light imaging requires stressful supplemental lighting, while thermal imaging lacks critical textural details. Although RGB-thermal (RGB-T) fusion alleviates some of these challenges, current systems often struggle with spatiotemporal misalignment and simplistic fusion techniques, resulting in redundancy and performance bottlenecks. This study introduces a low-stress, spatiotemporally synchronized RGB-T dual-modal imaging system combined with an end-to-end Dual-Stream Dead Chicken Detection Network (DS-DCDNet). By employing spectral beam splitting and multi-source synchronization, the hardware enables real-time, aligned RGB-T data acquisition. DS-DCDNet leverages adaptive feature self-fusion and dual-stream interactions, overcoming the limitations of manual parameter dependencies and improving detection accuracy by robustly integrating features at the representation level. Experimental results demonstrate that DS-DCDNet outperforms existing weighted and layer fusion methods, offering superior accuracy and stress-free detection capabilities. This research provides a scalable solution for high-precision automated dead chicken detection, meeting the growing demands of modern poultry farming. Related demonstration videos are available on YouTube (https://youtu.be/Pr1GjgX6kuw?si=kKRLe3PEDBlPQrSq) and YouKu (https://v.youku.com/video?vid=XNjQ3NTMwNjM2NA==) for reference.