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.