Water Level Recognition on Resource-Constrained Equipment using Pruned YOLOv5 and Template Matching

Abstract

This paper proposes a novel water level recognition method for detecting and recognizing water level rulers (WLR) using limited resources of the PX30 hardware development board. Our approach focuses on optimizing network efficiency, recognition speed, and accuracy. Specifically, we use the You Only Look Once Version 5 (YOLOv5) algorithm for object detection, and use the EagleEye algorithm to prune and optimize the model, so as to reduce the number of parameters and computation, and make the model more suitable for running on resource-constrained devices. Though our proposed pruned YOLOv5n@0.6 decrease 2.1% in mean average precision (mAP@0.5) but reduces the number of parameters and computation by 42% and 40%, respectively, compared to the YOLOv5n algorithm. To recognize the water level height, we apply the multi-template matching method to identify the reading number of the water level scale and the equal-ratio method to calculate the water level height. Our method achieves an average RED of 1.26 cm and RER of 5.8% on WLRD with only average 1.16s time cost per image on PX30. The experimental results show that the proposed method has high accuracy and calculation speed and is suitable for computing equipment with limited resources.