EGG-DEIM: Multi-Category Abnormal Egg Detection and Chicken Monitoring in Caged Poultry Farming

Cage-raised chicken farming is influenced by multiple environmental and husbandry factors, making it prone to various types of abnormal eggs, such as in-cage eggs, broken eggs, dirty eggs, and undersized eggs. Automated coop inspections require simultaneous monitoring of chickens and eggs to ensure farming safety. However, computer vision-based inspection robots are susceptible to interference from complex backgrounds, cage mesh obstructions, and uneven lighting, making it difficult for their abnormal egg recognition accuracy to meet practical requirements. To overcome this problem, this study proposes an improved object detection network, EGG-DEIM: It employs a phased dynamic data augmentation strategy, utilizing different data augmentation techniques at different training stages. It incorporates Partial Dynamic Convolution (PDConv) modules to reduce redundant features in the backbone network while mitigating interference from cage mesh occlusion; introduces a Spatial-Frequency Modulation (SMF) module to enhance the representation of high-, medium-, and low-frequency features in eggs; and incorporates a Pyramid Sparse Transformer (PST) module for multi-scale feature fusion to address feature transmission discontinuities in occluded scenes, thereby enabling multi-class classification of eggs and improving detection accuracy. Experimental results on the collected caged-poultry dataset show that EGG-DEIM attains 92.6% mAP@50 and 77.5% mAP@50:95 with only 7.43 GFLOPs and 5.04 M parameters, achieving higher accuracy within a limited computational budget than the baseline and mainstream detectors of comparable size.