A detection method for impact point water columns based on improved YOLO X

Abstract

This paper proposes an improved method to accurately and expediently detect water columns at the shells’ impact point. The suggested method combines a lightweight depthwise convolutional neural network (MobileNet v3) with the You Only Look Once X (YOLO X) algorithm, namely, YOLO X-m (MobileNet v3) that aims to simplify the network’s structure. Specifically, we used a weighted average pooling network and a spatial pyramid pooling network comprising multiple convolutional layers to retain as many features as possible. Moreover, we improve the activation and loss functions to reduce network calculations and afford better precision as well as fast and accurate water column detection. The experimental results reveal that YOLO X-m (MobileNet v3) ensures a good detection performance and adaptability to various light intensities, distances, and multiple water columns. Compared with the original YOLO X-m model, the improved network model achieves a 75.76% frames per second improvement and a 71.11% capacity reduction, while its AP50 decreases by only 1.29%. The proposed method is challenged against the single shot multibox detector and various YOLO variants, revealing its appealing accuracy, real-time detection performance, and suitability for practical applications and projects.