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The timely detection and treatment of abnormal chickens can greatly avoid the spread of infectious diseases in large-scale farming, and the common characterization state of abnormal chickens is that they close their eyes or squint. To achieve real-time monitoring of problem chickens in broiler farming, a YOLOv5-based chicken status detection algorithm Clite-YOLOv5 is proposed, which is based on YOLOv5 with the following improvements: the lite-CBC3 module incorporating CBAM is proposed and used to reconstruct the YOLOv5 backbone network to improve the detection capability of small targets in complex backgrounds; The improved Fuse-NMS suppression algorithm is used to reduce the false deletion rate of detection frames and fine-tune the final detection frames; the depth-separable convolution is used to replace the normal convolution in the backbone network, which reduces the number of parameters of the model and makes the model easier to deploy on mobile. Experimental results show that the proposed Clite-YOLOv5 algorithm has a mean average precision (mAP) of 92.88% and a video frame rate of 92 FPS, which outperforms other existing algorithms and can meet the demand for real-time monitoring of chicken status.