Evaluation of Duck Egg Hatching Characteristics with a Lightweight Multi-Target Detection Method

Abstract

Since it is difficult to accurately identify the fertilization and infertility status of multiple duck eggs on an incubation tray, and due to the lack of easy-to-deploy detection models, a novel lightweight detection architecture (LDA) based on the YOLOX-Tiny framework is proposed in this paper to identify sterile duck eggs with the aim of reducing model deployment requirements and improving detection accuracy. Specifically, the method acquires duck egg images through an acquisition device and augments the dataset using rotation, symmetry, and contrast enhancement methods. Then, the traditional convolution is replaced by a depth-wise separable convolution with a smaller number of parameters, while a new CSP structure and backbone network structure are used to reduce the number of parameters of the model. Finally, to improve the accuracy of the network, the method includes an attention mechanism after the backbone network and uses the cosine annealing algorithm in training. An experiment was conducted on 2111 duck eggs, and 6488 duck egg images were obtained after data augmentation. In the test set of 326 duck egg images, the mean average precision (mAP) of the method in this paper was 99.74%, which was better than the 94.92% of the YOLOX-Tiny network before improvement, and better than the reported prediction accuracy of 92.06%. The number of model parameters was only 1.93 M, which was better than the 5.03 M of the YOLOX-Tiny network. Further, by analyzing the concurrent detection of single 3 × 5, 5 × 7 and 7 × 9 grids, the algorithm achieved a single detection number of 7 × 9 = 63 eggs. The method proposed in this paper significantly improves the efficiency and detection accuracy of single-step detection of breeder duck eggs, reduces the network size, and provides a suitable method for identifying sterile duck eggs on hatching egg trays. Therefore, the method has good application prospects.