With the development of artificial intelligence, machine vision technology based on deep learning is an effective way to improve production efficiency. Because of the rapid update of the automobile manufacturing industry and the large variety of products, the learning time and the number of learning samples of the deep learning model are limited, which brings great difficulties to the recognition of components. Therefore, considering the economic benefits of enterprises, this paper proposes an intelligent component recognition method appropriate for small datasets, aiming to explore an automatic system for component recognition suitable for industrial manufacturing environments. The method completes the generation of the dataset through the system architecture with the potential for automation and the image cropping method based on feature detection and then designs a deep learning network based on coarse‐fine‐grained feature fusion to generate an intelligent recognition model of components. Finally, the designed network achieves an accuracy of 95.11%, and compared with the traditional classical network on multiple datasets, the designed network has better performance. Thus, the proposed method can improve the production flexibility of the automobile manufacturing industry and improve equipment intelligence.

Since the underground transportation of coal mainly relies on the mine conveyor belt to complete, the mine conveyor belt with large pieces of coal will affect transportation safety. Therefore, to address the problem of real‐time monitoring of lump coal, the method Ghost‐ECA‐Bi FPN (GEB) YOLOv5 for lump coal in the process of mining conveyor belt transportation is proposed based on a lightweight neural network and multisource information fusion. First, the image preprocessing is performed by adaptive histogram equalization, which reduces the influence of coal dust, dust, and uneven lighting on target monitoring. Second, the redundancy of the convolution process is exploited, and a lightweight neural network GhostNet is introduced to optimize the feature extraction process. In addition, combined with the efficient channel attention mechanism, the 1D convolution enables local cross‐channel information interaction, which can solve the problem of imbalance between model complexity and performance. Finally, the feature information of the three stages is fused using a weighted bidirectional feature pyramid network to enhance the generalization ability of the model. The experimental results show that the improved GEB YOLOv5 algorithm has obvious advantages. In terms of model structure, the number of network layers reduces by 36.97%, and the number of model structure parameters and floating‐point operations reduce by 64.53% and 69.14%, respectively. Moreover, the model volume reduces from 92.7 M to 33.0 M. Regarding the monitoring performance, the precision and recall rates improve by 1.19% and 1.11%, respectively. Furthermore, the real‐time performance improves from 68.34 FPS to 110.70 FPS. It can be seen that the problem of the model performance against the model complexity is effectively solved in this experiment and the real‐time monitoring of lump coal is realized.