The invention relates to a large-scale farm sign abnormal bird detection method, which comprises the following steps: pre-
processing the collected thermographic images for 
data annotation, then adopting an instance-based segmentation general framework (
Mask RCNN) target detection and instance 
separation method for 
feature extraction, pixel alignment, target positioning, classification and 
mask separation for thermal imaging images, by controlling the target detection number of a 
single image, improving the detection accuracy rate in each image. The method is an attempt in the field of target detection of current thermal imaging images, breaks the limitations brought by the traditional methods, and uses the superior performance of 
deep learning in image feature 
processing to improve the robustness and accuracy of a model. The feasibility of the 
deep learning method in the field of thermal imaging 
image detection is verified. At the same time, the method can also be extended to the 
livestock and other fields, thus improving the level of intelligence in the breeding industry in China.