Feather damage is a continuous health and welfare challenge among laying hens. Infrared thermography is a tool that can evaluate the changes in the surface temperature, derived from an inflammatory process that would make it possible to objectively determine the depth of the damage to the dermis. Therefore, the objective of this article was to develop an approach to feather damage assessment based on visible light and infrared thermography. Fusing information obtained from these two bands can highlight their strengths, which is more evident in the assessment of feather damage. A novel pipeline was proposed to reconstruct the RGB-Depth-Thermal maps of the chicken using binocular color cameras and a thermal infrared camera. The process of stereo matching based on binocular color images allowed for a depth image to be obtained. Then, a heterogeneous image registration method was presented to achieve image alignment between thermal infrared and color images so that the thermal infrared image was also aligned with the depth image. The chicken image was segmented from the background using a deep learning-based network based on the color and depth images. Four kinds of images, namely, color, depth, thermal and mask, were utilized as inputs to reconstruct the 3D model of a chicken with RGB-Depth-Thermal maps. The depth of feather damage can be better assessed with the proposed model compared to the 2D thermal infrared image or color image during both day and night, which provided a reference for further research in poultry farming.
Keywords: 3D reconstruction; RGB-D; deep learning; feather damage monitoring; infrared thermography; poultry.