Detection of Material on a Tray in an Automatic Assembly Line Based on Convolution Attention and Multitask Loss

This paper proposes an end-to-end first-stage pallet detection algorithm with short training time and high detection accuracy based on the pre-detection staged material detection algorithm. Not only can it detect known materials, blank areas and fixed material areas on pallets, but also unknown and unwanted materials that are mixed and misplaced on pallets on automated assembly lines. It employs ResNet18 as the backbone network, incorporates the Convolutional Block Attention Module (CBAM) to improve model stability and accuracy, and optimizes the detection model using the multitask loss function based on Complete-IoU(CIoU) and cross entropy. The experimental results show that when compared to the original phased detection algorithm using YOLOv5s trained on four NVIDIA GeForce RTX 2080 Ti for 18 h, the phased detection algorithm used in this study's first stage material detection algorithm achieves 98% overall recognition accuracy, which is 7% higher than the original phased algorithm (91^%). It also greatly reduces the model training time and allows rapid model deployment.