Abstract:
The pigeon industry is emerging after the "three birds" in the special poultry breeding. Pigeon breeding has also been well developed over more than 30 years in the animal husbandry industry in China. In this study, an improved RetinaNet model was proposed for the individual detection of caged pigeons during the breeding period. An accurate and rapid individual detection in the cage was realized to improve the breeding efficiency. The original image dataset was first collected in the Golden Green Pigeon Breeding Base in Xingning, Guangdong Province, China. The images were then enhanced by the vertically flipped, adding noise, and brightness. As such, the training and validation datasets were expanded to five times the original images. A total of 5 420 pigeon images were selected to be labeled, where 5 190 images were set as the training and validation set, and 230 as the test set. The model was improved using the RetinaNet. The Convolutional Block Attention Module (CBAM) was introduced before the classification and regression sub-networks. As such, the information of the feature map was enhanced for the better model. Two backbone networks (ResNet50 and ResNext50) were selected to compare to the Feature Pyramid Networks (FPN) with different layers on the individual detection of pigeons in the breeding period. 11 experiments were then performed on the same training, validation, and test set under the operating system of Ubuntu18.04 and graphics card: NVIDIA TITAN RTX. The commonly-used SSD and YOLOv3 in the one-stage object detection were selected to compare with the RetinaNet model framework in this case. Eight experiments were also carried out to optimize the model using the same dataset and training parameters. The results showed that the mAP of the original RetinaNet model structure was 65.44%, which was 3.21, 13.7, 4.47, 6.24, and 3.54 percentage points higher than that of SSD, YOLOv3, YOLOv4, YOLOv5s, and YOLOv5m, respectively. The overall detection effect of the original RetinaNet model was better than that of SSD and YOLOv3. Moreover, the number of FPN layers increased the detection scale, thereby effectively improving the recall of small objects. The missed detection of pigeon eggs was also reduced in the actual breeding environment using the original RetinaNet. Meanwhile, the CBAM was introduced before the classification and regression sub-network, in order to promote the detection effect on the adhesion pigeons. The improved RetinaNet model presented an average accuracy of 95.88%, 79.51%, and 67.29% on the test set of pigeons, squabs, and pigeon eggs, respectively, which were 2.16, 21.74, and 22.48 percentage points higher than the original RetinaNet model. There was also much more improvement in the average precision of adhesive squab and small pigeon eggs. Consequently, the improved model can also be expected to present the best performance. The real-time monitoring of individual changes can be achieved in the pigeon cage. The finding can provide the technical support for precision breeding, in order to timely detect and adjust the improper behavior of production management.