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In this paper, a high-precision hot-pressed light guide plate defect detection model based on improved YOLOv7 is proposed. The model strengthens the spatial correlation between background and foreground by fusing global context information. A densely connected convolutional network is used to enhance the feature extraction capability and mitigate problems such as gradient vanishing while ensuring the maximum information flow in the network. Further, adaptive spatial feature fusion is used in the feature fusion structure of the model; the adaptive spatial feature fusion structure compensates for the small targets that are difficult to extract in high dimensions from low dimensions, thus solving the problem of detecting small targets that are easy to lose. Finally, a self-constructed dataset is built using images of hot-pressed light guide plates collected from industrial sites, and a large number of experiments are conducted. Experimental results show that the defect detection model has a mean average precision (mAP) of 99.1% and a detection speed of 127 FPS. Compared with the mainstream surface defect target detection algorithms, while ensuring the detection speed, the accuracy rate has been significantly improved, and the accuracy rate and real-time can meet the requirements of the industrial field inspection of hot-pressed light guide plate.