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.