AFF-YOLOX: An improved lightweight YOLOX network to detect early hatching information of duck eggs

Abstract

Early hatching information detection of duck eggs is a crucial step in the duck egg hatching industry. In this study, we proposed a lightweight deep learning model AFF-YOLOX to detect the hatching information of multiple duck eggs on the standard hatching trays, meeting the speed requirements of real production. Based on YOLOX of networks structure, we added the AFF to the PA-FPN. Additionally, the input size of the image was changed according to the hatching tray and remove the redundant part of the network. The results showed that AFF-YOLOX could significantly improve the detection effect compared with the original YOLOX. The mAP was improved from 74.30% to 97.55%, the number of parameters was reduced from 99 M to 0.49 M, and the latency per image was decreased from 556 ms to 178 ms. AFF-YOLOX can detect abnormal eggs on incubation day 4 and can completely identify all abnormal eggs on incubation day 6. The results demonstrated that the optimization and lightweight methods we are effective and provides a technical reference for automatic detection of hatching information of duck eggs in the hatchery industry.