Abstract
Underwater fishing nets represent a danger faced by autonomous underwater vehicles (AUVs). To avoid irreparable damage to the AUV caused by fishing nets, the AUV needs to be able to identify and locate them autonomously and avoid them in advance. Whether the AUV can avoid fishing nets successfully depends on the accuracy and efficiency of detection. In this paper, we propose an object detection multiple receptive field network (MRF-Net), which is used to recognize and locate fishing nets using forward-looking sonar (FLS) images. The proposed architecture is a center-point-based detector, which uses a novel encoder-decoder structure to extract features and predict the center points and bounding box size. In addition, to reduce the interference of reverberation and speckle noises in the FLS image, we used a series of preprocessing operations to reduce the noises. We trained and tested the network with data collected in the sea using a Gemini 720i multi-beam forward-looking sonar and compared it with state-of-the-art networks for object detection. In order to further prove that our detector can be applied to the actual detection task, we also carried out the experiment of detecting and avoiding fishing nets in real-time in the sea with the embedded single board computer (SBC) module and the NVIDIA Jetson AGX Xavier embedded system of the AUV platform in our lab. The experimental results show that in terms of computational complexity, inference time, and prediction accuracy, MRF-Net is better than state-of-the-art networks. In addition, our fishing net avoidance experiment results indicate that the detection results of MRF-Net can support the accurate operation of the later obstacle avoidance algorithm.
Highlights
Introduction published maps and institutional affilAutonomous underwater vehicles (AUVs) are important tools used in ocean exploration
The network training of our experiments was run on an NVIDIA Quadro M5000 card, and the well-trained model was tested on an NVIDIA Jetson AGX Xavier embedded
Experimental Results and Analysis and the well-trained model was tested on an NVIDIA Jetson AGX Xavier embedded system module
Summary
Autonomous underwater vehicles (AUVs) are important tools used in ocean exploration. They can be applied in a wide range of tasks in marine investigation, resource exploration, and military fields. Underwater fishing nets represent a potentially fatal danger that AUVs often face in complex and unknown marine environments when performing their tasks. It is key to develop autonomous recognition of underwater fishing nets to improve the intelligence and autonomous survival ability of AUVs before they are deployed at sea. Inspired by the human vision system, object recognition for robots and other intelligent systems is usually conducted with optical sensors. The vision systems of AUVs mainly depend on sonar. Forward-looking sonar (FLS) is one of the main sensors that AUVs use to detect underwater objects.
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