基于Coordinate Attention和空洞卷积的异物识别

在我国工厂的工业化生产中, 带式运输机占有重要的地位, 但是在其运输物料的过程中, 常有木板、金属管、大型金属片等混入物料中, 从而对带式运输机的传送带造成损毁, 引起巨大的经济损失. 为了检测出传送带上的不规则异物, 设计了一种新的异物检测方法. 针对传统异物检测方法中存在的对于图像特征提取能力不足以及网络感受野相对较小的问题, 我们提出了一种基于coordinate attention和空洞卷积的单阶段异物识别方法. 首先, 网络利用coordinate attention机制, 使网络更加关注图像的空间信息, 并对图像中的重要特征进行了增强, 增强了网络的性能; 其次, 在网络提取多尺度特征的部分, 将原网络的静态卷积变为空洞卷积, 有效减少了常规卷积造成的信息损失; 除此之外, 我们还使用了新的损失函数, 进一步提高了网络的性能. 实验结果证明, 我们提出的网络能有效识别出传送带上的异物, 较好地完成异物检测任务.

Foreign Object Recognition Based on Coordinated Attention and Atrous Convolution

In the industrial production of factories in China, belt conveyors play an important role. However, in the process of transporting materials, wooden boards, metal pipes, large metal sheets, etc. are often mixed into the materials, causing damage to the conveyor belt of the belt conveyor and leading to huge economic losses. To detect irregular foreign objects on the conveyor belt, this study designs a new foreign object detection method. It proposes a single stage foreign object recognition method based on coordinated attention and atrous convolution to address the issues of insufficient image feature extraction ability and relatively small network receptive field in traditional foreign object detection methods. Firstly, the network utilizes the coordinated attention mechanism to make the network pay more attention to the spatial information of images and enhance important features in the images, improving the performance of the network. Secondly, while extracting multi-scale features from the network, the static convolution of the original network is transformed into an atrous convolution, effectively reducing the information loss caused by conventional convolution. In addition, the study also uses a new loss function, promoting the property of the network. The experimental results show that the proposed network can effectively identify foreign objects on the conveyor belt and effectively complete the foreign object detection task.