Aiming at the contradiction between the large number of parameters, high computational complexity and realtime requirements of defect detection models in industrial embedded scenarios, a CCS-YOLO lightweight defect detection network is proposed to be constructed by CSPPC module, CCFM module and SA_Detect fusion module. Its lightweight performance is verified by designing ablation experiments and comparative experiments. In order to enhance the feature extraction and expression capabilities when processing complex visual tasks and combine partial convolution operations to optimize the performance and efficiency of the model, the CSPPC module is used. The CCFM module is used to fuse features of different scales to improve the model’s adaptability to scale changes and the ability to detect small-scale objects. The SA_Detect module that fuses shared convolutions is used to further reduce the number of model parameters and achieve model lightweight, which effectively improves feature expression, target positioning and classification performance. The experimental results show that compared with YOLOv8n, the model size, computational complexity and weight parameters of the CCS-YOLO model are reduced by 56.7%, 51.9% and 54.0% respectively, with a significant lightweight effect.The detection speed is maintained above 34 fps when deployed on the RK3568 embedded platform, and the real-time performance is verified, which is practical and efficient. It can be seen that the application cost-effectiveness of the system has been improved, effectively overcoming the shortcomings caused by a slight decrease in accuracy. The constructed defect detection network CCS-YOLO can solve the problem of resource constraints in industrial embedded scenarios and realize a feasible solution for low-computing power devices to achieve high performance, real-time and lightweight, which has important engineering value.