LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection

Tu, Xiaohan; Xu, Cheng; Liu, Siping; Lin, Shuai; Chen, Lipei; Xie, Guoqi; Li, Renfa

doi:10.3390/s20216387

Cited by 16 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method makes the channels for the input images reduce and makes multiple images train at the same time. In addition, it can reduce the computational complexity of DL models [ 18 ]. By using this technique, we could input 4 images, including 0°, 30°, 60°, and 90° MIP, into Xception that needs images composed of 3 channels ( Figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer

et al. 2022

View full text Add to dashboard Cite

Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study investigated whether a DL model using images with multiple degrees of PET maximum-intensity projection (MIP) images contributes to increase diagnostic accuracy for PET/CT image classification in BC. We retrospectively gathered 400 images of 200 BC and 200 non-BC patients for training data. For each image, we obtained PET MIP images with four different degrees (0°, 30°, 60°, 90°) and made two DL models using Xception. One DL model diagnosed BC with only 0-degree MIP and the other used four different degrees. After training phases, our DL models analyzed test data including 50 BC and 50 non-BC patients. Five radiologists interpreted these test data. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Our 4-degree model, 0-degree model, and radiologists had a sensitivity of 96%, 82%, and 80–98% and a specificity of 80%, 88%, and 76–92%, respectively. Our 4-degree model had equal or better diagnostic performance compared with that of the radiologists (AUC = 0.936 and 0.872–0.967, p = 0.036–0.405). A DL model similar to our 4-degree model may lead to help radiologists in their diagnostic work in the future.

show abstract

Section: Methodsmentioning

confidence: 99%

Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The development of a novel drainage inspection robot is mentioned in [ 25 ], where authors propose strategies for the collision avoidance and stability control of the drainage inspection robot. Additionally, an inspection of contacts on overhead cable systems for high-speed rails using inspection robots is discussed in [ 26 ]. Leveraging on the precedence on effective usage of robots for complex inspection strategies and the need to eliminate risk and inefficiency factors involved in false-ceiling inspection gives rise to a new modality of inspection, which is the usage of lightweight inspection robots.…”

Section: Introductionmentioning

confidence: 99%

Towards an Optimal Footprint Based Area Coverage Strategy for a False-Ceiling Inspection Robot

Pathmakumar

Sivanantham

Padmanabha

et al. 2021

Sensors

View full text Add to dashboard Cite

False-ceiling inspection is a critical factor in pest-control management within a built infrastructure. Conventionally, the false-ceiling inspection is done manually, which is time-consuming and unsafe. A lightweight robot is considered a good solution for automated false-ceiling inspection. However, due to the constraints imposed by less load carrying capacity and brittleness of false ceilings, the inspection robots cannot rely upon heavy batteries, sensors, and computation payloads for enhancing task performance. Hence, the strategy for inspection has to ensure efficiency and best performance. This work presents an optimal functional footprint approach for the robot to maximize the efficiency of an inspection task. With a conventional footprint approach in path planning, complete coverage inspection may become inefficient. In this work, the camera installation parameters are considered as the footprint defining parameters for the false ceiling inspection. An evolutionary algorithm-based multi-objective optimization framework is utilized to derive the optimal robot footprint by minimizing the area missed and path-length taken for the inspection task. The effectiveness of the proposed approach is analyzed using numerical simulations. The results are validated on an in-house developed false-ceiling inspection robot—Raptor—by experiment trials on a false-ceiling test-bed.

show abstract

“…LiDAR systems can quickly and accurately obtain a large number of 3D point clouds and extract valuable spatial data with details that cannot be achieved by previous patrol detection techniques [ 13 ]. It has a unique advantage in the acquisition of 3D information of railways in complex and even dangerous areas and has a great application prospect in the detection of OCS [ 14 , 15 ]. Therefore, the classification of OCS from the point cloud is an effective method for OCS detection [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Railway Overhead Contact System Point Cloud Classification

Xiao

Chen

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

As the railway overhead contact system (OCS) is the key component along the high-speed railway, it is crucial to detect the quality of the OCS. Compared with conventional manual OCS detection, the vehicle-mounted Light Detection and Ranging (LiDAR) technology has advantages such as high efficiency and precision, which can solve the problems of OCS detection difficulty, low efficiency, and high risk. Aiming at the contact cables, return current cables, and catenary cables in the railway vehicle-mounted LiDAR OCS point cloud, this paper used a scale adaptive feature classification algorithm and the DBSCAN (density-based spatial clustering of applications with noise) algorithm considering OCS characteristics to classify the OCS point cloud. Finally, the return current cables, catenary cables, and contact cables in the OCS were accurately classified and extracted. To verify the accuracy of the method presented in this paper, we compared the experimental results of this article with the classification results of TerraSolid, and the classification results were evaluated in terms of four accuracy indicators. According to statistics, the average accuracy of using this method to extract two sets of OCS point clouds is 99.83% and 99.89%, respectively; the average precision is 100% and 99.97%, respectively; the average recall is 99.16% and 99.42%, respectively; and the average overall accuracy is 99.58% and 99.69% respectively, which is overall better than TerraSolid. The experimental results showed that this approach could accurately and quickly extract the complete OCS from the point cloud. It provides a new method for processing railway OCS point clouds and has high engineering application value in railway component detection.

show abstract

LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection

Cited by 16 publications

References 28 publications

Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer

Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer

Towards an Optimal Footprint Based Area Coverage Strategy for a False-Ceiling Inspection Robot

Railway Overhead Contact System Point Cloud Classification

Contact Info

Product

Resources

About