Side scan sonar(SSS)is an important means to detect and locate seafloor targets.Autonomous underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the t...Side scan sonar(SSS)is an important means to detect and locate seafloor targets.Autonomous underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the target for observers.The target feature information of an SSS image is similar to the background information,and a small target has less pixel information;therefore,accu-rately identifying and locating small targets in SSS images is challenging.We collect the SSS images of iron metal balls(with a diameter of 1m)and rocks to solve the problem of target misclassification.Thus,the dataset contains two types of targets,namely,‘ball’and‘rock’.With the aim to enable AUVs to accurately and automatically identify small underwater targets in SSS images,this study designs a multisize parallel convolution module embedded in state-of-the-art Yolo5.An attention mechanism transformer and a convolutional block attention module are also introduced to compare their contributions to small target detection accuracy.The performance of the proposed method is further evaluated by taking the lightweight networks Mobilenet3 and Shufflenet2 as the backbone network of Yolo5.This study focuses on the performance of convolutional neural networks for the detection of small targets in SSS images,while another comparison experiment is carried out using traditional HOG+SVM to highlight the neural network’s ability.This study aims to improve the detection accuracy while ensuring the model efficiency to meet the real-time working requirements of AUV target detection.展开更多
针对建筑机器人在复杂环境下自主导航过程中的安全性、通过性等问题,提出了一种应用BIM(building information model)信息与激光雷达获取的现实环境信息进行导航并保证路径最优的方法。根据BIM信息优化A*算法,考虑建筑机器人的通过性与...针对建筑机器人在复杂环境下自主导航过程中的安全性、通过性等问题,提出了一种应用BIM(building information model)信息与激光雷达获取的现实环境信息进行导航并保证路径最优的方法。根据BIM信息优化A*算法,考虑建筑机器人的通过性与安全性并删除了路径中的冗余节点,在节点间根据雷达信息优化动态窗口法,有效保证了路径最优,提升了安全性、缩短了运行时间并减少了转折角度。加入超宽频定位模块,能够很好地消除机器人移动产生的累计误差。实验结果表明,改进后A*算法的搜索时间比改进前减少了92%,路径转折角度减少50%,路径长度比原始动态窗口法减少13.5%,所需时间仅比无障碍物时增加3.7%。展开更多
基金supported by the National Key Research and Development Program of China(No.2016YFC0301400).
文摘Side scan sonar(SSS)is an important means to detect and locate seafloor targets.Autonomous underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the target for observers.The target feature information of an SSS image is similar to the background information,and a small target has less pixel information;therefore,accu-rately identifying and locating small targets in SSS images is challenging.We collect the SSS images of iron metal balls(with a diameter of 1m)and rocks to solve the problem of target misclassification.Thus,the dataset contains two types of targets,namely,‘ball’and‘rock’.With the aim to enable AUVs to accurately and automatically identify small underwater targets in SSS images,this study designs a multisize parallel convolution module embedded in state-of-the-art Yolo5.An attention mechanism transformer and a convolutional block attention module are also introduced to compare their contributions to small target detection accuracy.The performance of the proposed method is further evaluated by taking the lightweight networks Mobilenet3 and Shufflenet2 as the backbone network of Yolo5.This study focuses on the performance of convolutional neural networks for the detection of small targets in SSS images,while another comparison experiment is carried out using traditional HOG+SVM to highlight the neural network’s ability.This study aims to improve the detection accuracy while ensuring the model efficiency to meet the real-time working requirements of AUV target detection.
文摘针对建筑机器人在复杂环境下自主导航过程中的安全性、通过性等问题,提出了一种应用BIM(building information model)信息与激光雷达获取的现实环境信息进行导航并保证路径最优的方法。根据BIM信息优化A*算法,考虑建筑机器人的通过性与安全性并删除了路径中的冗余节点,在节点间根据雷达信息优化动态窗口法,有效保证了路径最优,提升了安全性、缩短了运行时间并减少了转折角度。加入超宽频定位模块,能够很好地消除机器人移动产生的累计误差。实验结果表明,改进后A*算法的搜索时间比改进前减少了92%,路径转折角度减少50%,路径长度比原始动态窗口法减少13.5%,所需时间仅比无障碍物时增加3.7%。
