A Real-time Algorithm for Visual Detection of High-speed Unmanned Surface Vehicle Based on Deep Learning

Zhou, Zhiguo; Yu, Siyu; Liu, Kaiyuan

doi:10.1109/icsidp47821.2019.9173240

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Vision-based detection methods are the ones that are mainly used for water surface object detection. For example, the method proposed in [11] is based on MobileNet for feature extraction and SSD for fast multi-scale detection to achieve realtime marine object detection of high-speed USVs. Zhang et al [12] proposed a method for marine object detection and tracking based on improved YOLOv3 and used their method on a real USV experiment platform.…”

Section: Object Detection On Water Surfacesmentioning

confidence: 99%

A Millimeter-Wave Radar-Aided Vision Detection Method for Water Surface Small Object Detection

Zhu,

Yang,

Cheng

2023

JMSE

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Unmanned surface vehicles (USVs) have wide applications in marine inspection and monitoring, terrain mapping, and water surface cleaning. Accurate and robust environment perception ability is essential for achieving autonomy in USVs. Small object detection on water surfaces is an important environment perception task, typically achieved by visual detection using cameras. However, existing vision-based small object detection methods suffer from performance degradation in complex water surface environments. Therefore, in this paper, we propose a millimeter-wave (mmWave) radar-aided vision detection method that enables automatic data association and fusion between mmWave radar point clouds and images. Through testing on real-world data, the proposed method demonstrates significant performance improvement over vision-based object detection methods without introducing more computational costs, making it suitable for real-time application on USVs. Furthermore, the image–radar data association model in the proposed method can serve as a plug-and-play module for other object detection methods.

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Section: Object Detection On Water Surfacesmentioning

confidence: 99%

A Millimeter-Wave Radar-Aided Vision Detection Method for Water Surface Small Object Detection

Zhu,

Yang,

Cheng

2023

JMSE

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“…For ship detection, a series of convolution neural networks-based methods have been proposed [2][3][4][5][6][7][8]. However, most methods are developed under normal sea conditions, the ship detection accuracy would be reduced under haze condition for the degraded image cannot provide e cient features.…”

Section: Introductionmentioning

confidence: 99%

Dual-Channel and Two-Stage Dehazing Network for Promoting Ship Detection in Visual Perception System

Liu

Baijun

2022

Mathematical Problems in Engineering

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Maritime video surveillance of visual perception system has become an essential method to guarantee unmanned surface vessels (USV) traffic safety and security in maritime applications. However, when visual data are collected in a foggy marine environment, the essential optical information is often hidden in the fog, potentially resulting in decreased accuracy of ship detection. Therefore, a dual-channel and two-stage dehazing network (DTDNet) is proposed to improve the clarity and quality of the image to guarantee reliable ship detection under foggy conditions. Specifically, an upper and lower sampling structure is introduced to expand the original two-stage dehazing network into a two-channel network, to further capture the image features from different scale. Meanwhile, the attention mechanism is combined to provide different weights for different feature maps to maintain more image information. Furthermore, the perceptual function is constructed with the MSE-based loss function, so that it can better reduce the gap between the dehazing image and the unhazy image. Extensive experiments show that DTDNet has a better dehazing performance on both visual effects and quantitative index than other state-of-the-art dehazing networks. Moreover, the dehazing network is combined with the problem of ship detection under a sea-fog environment, and experiment results demonstrate that our network can be effectively applied to improve the visual perception performance of USV.

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FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters

Cheng

Zhu

Jiang

et al. 2021

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

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A Real-time Algorithm for Visual Detection of High-speed Unmanned Surface Vehicle Based on Deep Learning

Cited by 4 publications

References 11 publications

A Millimeter-Wave Radar-Aided Vision Detection Method for Water Surface Small Object Detection

A Millimeter-Wave Radar-Aided Vision Detection Method for Water Surface Small Object Detection

Dual-Channel and Two-Stage Dehazing Network for Promoting Ship Detection in Visual Perception System

FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters

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