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To address the limited pixel coverage, relatively weak features, and uneven distribution of defects in conveyor belt images, a conveyor belt defect detection system is designed, accompanied by a label assignment strategy based on Gaussian Mixture Model. Leveraging the Gaussian distribution prior information following the feature receptive fields, a Gaussian model is constructed to adaptively capture conveyor belt defects of varying scales through dynamic adjustment mechanisms, thereby enhancing the detection capability for minor defects effectively. Replacing the Intersection over Union with receptive field distance, the similarity between Gaussian receptive fields and true labels is measured, facilitating sample allocation based on their similarity, thereby improving the accuracy of sample assignment effectively. Utilizing Gaussian Mixture Model and Expectation-Maximization algorithm for probability distribution fitting, adaptive allocation of positive and negative samples for feature points is achieved, effectively mitigating the issue of missed detections caused by faint features of minor defects. Results demonstrate a significant enhancement in the accuracy of conveyor belt defect detection attributed to the Gaussian Mixture Model label assignment strategy, exhibiting a 3.8% improvement in accuracy compared to the baseline network.