Lightweight identification of retail products based on improved convolutional neural network

Due to the similar appearances among many retail products, it is a big challenge to identify the product with high accuracy and low computational cost in smart retail scenes. In this paper, we proposed a lightweight retail product identification and localization method based on an improved convolutional neural network. First, we use group convolution and deep separable convolution to optimize the structure of the backbone network and reduce the amount of calculation. Second, the multiscale structure was adjusted to optimal scales. We further use the k-means clustering algorithm to re-cluster six anchors with different sizes. Third, we introduced spatial pyramid pooling (SPP) to replace pooling by convolution to effectively improve the robustness against image distortion, such as cropping and scaling. Finally, we use mosaic data enhancement method to improved the robustness of the network. Experiments on the RPC dataset show that, compared with YOLOv5, the number of parameters is reduced by 1/6.4 times, and FLOPs is reduced by 1/9 times. Experiments on the DeepBlue Retail Dataset show that compared with YOLOv5, the number of parameters is reduced by 1/7.8 times, and FLOPs is reduced by 1/9.3 times. Realtime evaluation under the same hardware show that the FPS of the proposed model is 123 in the forward inference test, while the FPS of the YOLOv5 model under the same conditions is 58.