Manipulator-based autonomous inspections at road checkpoints: Application of faster YOLO for detecting large objects

Shi, Qingxin; Li, Changsheng; Guo, Baoqiao; Wang, Yonggui; Tian, Huanyu; Hao, Wen; Meng, Fansheng; Duan, Xingguang

doi:10.1016/j.dt.2021.04.004

Cited by 13 publications

(6 citation statements)

References 37 publications

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“…The reported underwater robots are used to take the concrete damage videos and images. CODEBRIM (Mundt et al., 2019), the open dataset of bridge damages, are used to further extend our dataset of more than 1000 damage images of underwater concrete structures (Shi et al., 2022). Figure 6a indicates the three types of damages from the datasets that reveal different damage patterns.…”

Section: Yolo‐underwater Model In the Mo‐shm Systemmentioning

confidence: 99%

“…Taking advantage of the rapid development of underwater robots, various field exploration has been carried out such as underwater SHM (Galceran et al., 2015; Neto et al., 2014), extreme disaster investigation (Rafiei & Adeli, 2017b; Sahoo et al., 2019), and so forth. Different SHM systems have been developed based on underwater robots (Guerneve et al., 2018; Shi et al., 2022). Underwater robots are actuated by various methods, such as multi‐propeller cooperative propulsion (Salazar et al., 2019), hydraulic propulsion (Roper et al., 2011), vector propulsion (Du et al., 2018), and waterjets propulsion (Ba et al., 2013).…”

Section: Introductionmentioning

confidence: 99%

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Vision‐based real‐time marine and offshore structural health monitoring system using underwater robots

Jiao

Zhang

et al. 2023

Computer aided Civil Eng

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Structural health monitoring (SHM) plays an increasingly vital role in guaranteeing the operation success of marine and offshore (MO) structures while reducing their risks of structural failure. In this study, a real‐time MO‐SHM system based on the highly controllable underwater robots is developed, which is functionalized with three modules, that is, underwater monitoring robots, vision‐based image processing and analyzing, and time‐dependent damage assessing and early warning. The robotic module is actuated by the hybrid driving method that combines the combustion‐based actuators (ejection) and propeller thrusters (propulsion) for transient actuation ability and well reliability in complex underwater environments. The image processing and analyzing module is conducted based on the You Only Look Once (YOLO)‐Underwater model that is expanded by the transfer learning and attention mechanisms and the underwater preconditioning and warning correction added for the specific underwater concrete identification. The damage assessing and early warning module is achieved by the time‐dependent hybrid analytic hierarchy process method that is integrated with the ordered weighted averaging and entropy weight method for more objective assessment and feedback. The reported MO‐SHM system provides design guidance for the next‐generation multifunctional underwater devices that combine analyzing and early warning for underwater concrete structures.

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Section: Yolo‐underwater Model In the Mo‐shm Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vision‐based real‐time marine and offshore structural health monitoring system using underwater robots

Jiao

Zhang

et al. 2023

Computer aided Civil Eng

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“…According to their performance analysis, the presented algorithm works better than does Faster R-CNN by achieving an accuracy of 93.5%. In addition to the quality inspection in manufacturing, YOLO is also being studied in various fields such as infrastructure management [36,37].…”

Section: Ai Technologies Applied For Industrial Applicationsmentioning

confidence: 99%

A Data-Centric Approach to Design and Analysis of a Surface-Inspection System Based on Deep Learning in the Plastic Injection Molding Industry

Lee²,

Lee³

et al. 2021

Processes

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Manufacturers are eager to replace the human inspector with automatic inspection systems to improve the competitive advantage by means of quality. However, some manufacturers have failed to apply the traditional vision system because of constraints in data acquisition and feature extraction. In this paper, we propose an inspection system based on deep learning for a tampon applicator producer that uses the applicator’s structural characteristics for data acquisition and uses state-of-the-art models for object detection and instance segmentation, YOLOv4 and YOLACT for feature extraction, respectively. During the on-site trial test, we experienced some False-Positive (FP) cases and found a possible Type I error. We used a data-centric approach to solve the problem by using two different data pre-processing methods, the Background Removal (BR) and Contrast Limited Adaptive Histogram Equalization (CLAHE). We have experimented with analyzing the effect of the methods on the inspection with the self-created dataset. We found that CLAHE increased Recall by 0.1 at the image level, and both CLAHE and BR improved Precision by 0.04–0.06 at the bounding box level. These results support that the data-centric approach might improve the detection rate. However, the data pre-processing techniques deteriorated the metrics used to measure the overall performance, such as F1-score and Average Precision (AP), even though we empirically confirmed that the malfunctions improved. With the detailed analysis of the result, we have found some cases that revealed the ambiguity of the decisions caused by the inconsistency in data annotation. Our research alerts AI practitioners that validating the model based only on the metrics may lead to a wrong conclusion.

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“…To obtain a more robust and accurate detection model, the following literature provides different improvement methods. Shi et al 9 introduced GIoU into K-means++ to obtain better anchors. Manuel et al 10 used an evolutionary algorithm to search for optimal region-based anchors.…”

Section: Introductionmentioning

confidence: 99%

The research of a novel WOG-YOLO algorithm for autonomous driving object detection

Wei

2023

Sci Rep

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Object detection has been one of the critical technologies in autonomous driving. To improve the detection precision, a novel optimization algorithm is presented to enhance the performance of the YOLOv5 model. First, by improving the hunting behavior of the grey wolf algorithm(GWO) and incorporating it into the whale optimization algorithm(WOA), a modified whale optimization algorithm(MWOA) is proposed. The MWOA leverages the population’s concentration ratio to calculate $$p_h$$ p h for selecting the hunting branch of GWO or WOA. Tested by six benchmark functions, MWOA is proven to possess better global search ability and stability. Second, the C3 module in YOLOv5 is substituted by G-C3, and an extra detection head is added, thus a highly optimizable detection G-YOLO network is constructed. Based on the self-built dataset, 12 initial hyperparameters in the G-YOLO model are optimized by MWOA using a score fitness function of compound indicators, thus the final hyperparameters are optimized and the whale optimization G-YOLO (WOG-YOLO) model is obtained. In comparison with the YOLOv5s model, the overall mAP increases by 1.7$$\%$$ % , the mAP of pedestrians increases by 2.6$$\%$$ % and the mAP of cyclists increases by 2.3$$\%$$ % .

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Manipulator-based autonomous inspections at road checkpoints: Application of faster YOLO for detecting large objects

Cited by 13 publications

References 37 publications

Vision‐based real‐time marine and offshore structural health monitoring system using underwater robots

Vision‐based real‐time marine and offshore structural health monitoring system using underwater robots

A Data-Centric Approach to Design and Analysis of a Surface-Inspection System Based on Deep Learning in the Plastic Injection Molding Industry

The research of a novel WOG-YOLO algorithm for autonomous driving object detection

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