Fine-grained detection of caged-hen head states using adaptive Brightness Adjustment in combination with Convolutional Neural Networks
Keywords:
Grid R-CNN, squeeze-and-excitation, Depthwise Over-parameterized Convolutional, adaptive brightness adjustment, fine-grained detectionAbstract
Timely identification and tracking of abnormal hens in stacked cages are of great significance for precision treatment and the elimination of sick individuals. The head features of the caged-hens are used to overcome observation difficulties caused by the cage and feathers blocking, but it is still hard to identify similar head states. To solve this problem, a fine-grained detection of caged-hens head states was developed using adaptive Brightness Adjustment in combination with Convolutional Neural Networks (FBA-CNN). Grid Region-based CNN (R-CNN), a convolution neural network (CNN), was optimized with the Squeeze-and-Excitation (SE) and Depthwise Over-parameterized Convolutional (DO-Conv) to detect layer heads from cages and to accurately cut them as single-head images. The brightness of each single-head image was adjusted adaptively and classified through the deep convolution neural network based on SE-Resnet50. Finally, we returned to the original image to realize multi-target detection with coordinate mapping. The results showed that the AP@0.5 of layer head detection using the optimized Grid R-CNN was 0.947, the accuracy of classification with SE-Resnet50 was 0.749, the F1 score was 0.637, and the mAP@0.5 of FBA-CNN was 0.846. In summary, this automated method can accurately identify different layer head states in layer cages to provide a basis for follow-up studies of abnormal behavior including dyspnea and cachexia. Keywords: Grid R-CNN, squeeze-and-excitation, Depthwise Over-parameterized Convolutional, adaptive brightness adjustment, fine-grained detection DOI: 10.25165/j.ijabe.20231603.7507 Citation: Chen J, Ding Q A, Yao W, Shen M X, Liu L S. Fine-grained detection of caged-hens head states using adaptive Brightness Adjustment in combination with Convolutional Neural Networks. Int J Agric & Biol Eng, 2023; 16(): 16(3): 208–216.References
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