An Industrial Meter Detection Method Based on Lightweight YOLOX-CAlite

With the application of inspection robots, the demand for automatic detection and identification of pointers in pumping stations, substations, and laboratories has increased. This paper proposes the YOLOX-CAlite meter detection algorithm to address the lack of performance in detecting targets involved in the specific meter detection process. The core of the YOLOX-CAlite is improving the drawbacks of the original algorithm with its large backbone network, a large number of parameters, and large calculation volume. It mainly consists of using data augmentation on the input side. We removed the Focus structure from the original YOLOX and replace it with a convolutional pooling form. The backbone was replaced with Shufflenet v2 and the Ghost module was introduced into the neck to improve real-time performance. Meanwhile, the neck was changed to BiFPN structure to enhance feature fusion. coordinate attention was introduced in front of the detection head to enhance feature extraction. Finally, the binary cross-entropy loss in YOLOX was replaced by focal loss, and the multi-task joint loss of the algorithm was optimized using the complete-IoU loss function. The experimental results on our dataset showed the improved YOLOX-CAlite achieved an AP of 90.4%. Compared to the YOLOX-s and YOLOv5, YOLOX-CAlite improved the AP by 1.4% and 2.2%. And the gflops have been reduced massively, from 26.6 gflops to 8.8 gflops, a reduction of about 68%. The detection speed increased by 25% while the number of parameter sizes is reduced. The model size has been reduced from 17.2 M in YOLOX-s to 4.89 M, a reduction of about 71%. The inspection robots can identify the meter better in the following works.