An efficient MobileNetv3 and YOLOv4 integrated network multi-dimensional real-time detection method for longitudinal tearing of conveyor belt was proposed to aim at the problem of single dimension and high complexity of model in the detection of the longitudinal tearing target of the conveyor belt. The lightweight network MobileNetv3 based on the object detection algorithm of YOLOv4 was used to replace CSPDarknet53 as the backbone network of YOLOv4. The ECSNet was constructed by combining efficient channel domain ECA model and spatial transformer network (STNet). The ECSNet was embedded in MobileNetv3 to improve the attention of model to space and channels. The deep separable convolution block was introduced to replace the 3*3 convolution in the network and the Prediction Heads of YOLOv4 were reduced to two scales. The network model was lightened, the complexity and training difficulty were reduced and ECSMv3_YOLOv4 model was built. The K-means was used to cluster six Anchors to predict the height and width of the bounding box, which improved the detection performance of the network for surface tearing. The multi-dimensional intelligent inspection prototype of belt conveyor was developed, the longitudinal tear data set of multi-dimensional surface of conveyor belt was collected and made. The training, testing, identification and positioning experiments of network model were carried out. The results show that the average detection accuracy of the proposed algorithm in the test set is 97.8%, the recognition speed is 37 frame/s and the computational quantity and parameter quantity of the model are 4.882 G and 8.851 M respectively. By testing the effects of different network models and changing the light intensity, the method embodies the advantages of high detection accuracy, fast speed, lightweight and the proposed algorithm has stronger adaptability and anti-interference ability.