Lightweight convolutional neural network for fast visual perception of storage location status in stereo warehouse

Accurate storage location status data is an important input for location assignment in the inbound stage. Traditional Internet of Things (IoT) identification technologies require high costs and are easily affected by warehouse environments. A lightweight convolutional neural network is proposed for perceiving storage status to achieve high stability and low cost of location availability monitoring. Based on the existing You Only Look Once (YOLOv5) algorithm, the Hough transform is used in the pre-processing to implement tilt correction on the image to improve the stability of object localization. Then the feature extraction unit CBlock is designed based on a new depthwise separable convolution in which the convolutional block attention module is embedded, focusing on both channel and spatial information. The backbone network is constructed by stacking these CBlock blocks to compress the computational cost. The improved neck network adds cross-layer information fusion to reduce the information loss caused by sampling and ensure perceptual accuracy. Moreover, the penalty metric is redefined by SIoU, which considers the vector angle of the bounding box regression and improves the convergence speed and accuracy. The experiments show that the proposed model achieves successful results for storage location status perception in stereo warehouse.