YOLOv3-DPFIN: A Dual-Path Feature Fusion Neural Network for Robust Real-Time Sonar Target Detection

Real-time detection of sonar target plays a vital role in the underwater research field. Conventionaldeep learning methods need large quantities of sonar images as the sample for model training, and they cannot ensure detection speed and feature extraction ability simultaneously. For sonar dataset with small effective sample and low Signal-to-Noise Ratios (SNR), an improved YOLOv3 algorithm for real-time detection called as YOLOv3-DPFIN is proposed. The objective of the proposed YOLOv3-DPFIN is to accomplish the accurate detection of noise-intensive multi-category sonar targets with minimum time consumption. The proposed model conducts efficient feature extraction via the Dual-Path Network (DPN) module and the fusion transition module, and adopts a dense connection method to improve multi-scale prediction, which can complete precise object classification and location. The experimental results show that the algorithm achieves 84.4% mAP75 with 56fps on a Nvidia Titan Xp, when testing on the sonar dataset and using the new VOC2012 mAP standard, which can meet the requirement of robust real-time detection for both raw and noised sonar targets. Moreover, the precision and speed of the proposed YOLOv3-DPFIN are superior to the original YOLOv3 model and state-of-the-art improved SSD models.