Methods to Detect Road Features for Video-Based In-Vehicle Navigation Systems

Choi, Kyoung-Ho; Park, Soon Young; Kim, Seong Hoon; Lee, Ki Sung; Park, Jeong Ho; Cho, Seong Ik; Park, Jong Hyun

doi:10.1080/15472450903386005

Cited by 24 publications

(7 citation statements)

References 17 publications

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“…In order to verify the accuracy and robustness of the implementation of the algorithm in this paper, we evaluate the misalignment detection method in the paper through two parts of experiments, static acquisition and dynamic acquisition, the static acquisition is the image of the web carpet in nonoperational state in the laboratory, and the dynamic acquisition is the image of the web carpet monitoring video frames in operation at a real industrial site. Since the press section of a paper machine is subjected to continuous mechanical stresses and will undergo roll pressing, wear, compaction and contamination with the highest risk of misalignment [20], we used the Press felt in the press section of a paper machine as an example for experimental validation.…”

Section: Resultsmentioning

confidence: 99%

Research on Misalignment Detection of Paper Machine Clothing Based on Computer Vision

Hu,

Wang

2024

AJST

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Paper machine clothing (PMC) is a specialized material essential to the paper industry, forming an integral part of paper-making equipment. Deviations in the net carpet, in particular, can lead to pleats along the edge or in the middle of the net carpet, making it challenging to spread and often resulting in broken pleats. These pleats contribute to increased paper-making costs. To address this issue of paper machine clothing misalignment, this study proposes a monitoring approach based on computer vision. Initially, image filtering and edge enhancement are applied to the video images obtained from the complex manufacturing environment of a paper mill. The Canny operator is then utilized for edge detection to obtain a binary image of the edges. Subsequently, Hough transform is employed to detect the edges of the net blanket based on the straight-line characteristics of paper machine clothing. Additionally, regions of interest (ROI) are established to enhance processing speed and reliability. If broken straight line segments are observed at the edge of the mesh blanket, these are combined using least squares fitting to form a complete straight line. Finally, deviation in the paper machine clothing is monitored by analyzing the slope value of the fitted edge straight line and its distance from the x-axis of the outer roller edge. Test results demonstrate that using the method proposed in this study, it is possible to determine both the angle of net blanket deviation and its distance from the rollers' edges. Through validation using laboratory and mill data, the algorithm presented in this study effectively reflects these parameters.

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Section: Resultsmentioning

confidence: 99%

Research on Misalignment Detection of Paper Machine Clothing Based on Computer Vision

Hu,

Wang

2024

AJST

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“…Its main drawback is the need of a priori knowledge about the road network. In its actual form, [11,33] the BN allows the detection of road intersection without searching for their types.…”

Section: Road Intersection Detectionmentioning

confidence: 99%

Road intersection detection and classification using hierarchical SVM classifier

2014

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In this paper, a hierarchical multi-classification approach using support vector machines (SVM) has been proposed for road intersection detection and classification. Our method has two main steps. The first involves the road detection. For this purpose, an edge-based approach has been developed using the bird's eye view image which is mapped from the perspective view of the road scene. Then, the concept of vertical spoke has been introduced for road boundary form extraction. The second step deals with the problem of road intersection detection and classification. It consists on building a hierarchical SVM classifier of the extracted road forms using the unbalanced decision tree architecture. Many measures are incorporated for good evaluation of the proposed solution. The obtained results are compared to those of Choi et al. (2007).

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“…4. Lane information can be extracted in many different ways; for example, through a Hough transform or an inverse perspective transform, or a conventional lane-detection algorithm [28]. Here, HL W denotes the width of the headlights, and the width of the left and the right headlights are considered to be equal; HL H , HL D , and L c (x, y) denote the height of the headlights, the distance between the left and right headlights, and the center of a detected pair of headlights, respectively.…”

Section: Downhill Simplex Algorithm For Headlights Detectionmentioning

confidence: 99%

State Machine and Downhill Simplex Approach for Vision-Based Nighttime Vehicle Detection

Choi

Kim

et al. 2014

ETRI J

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In this paper, a novel vision-based nighttime vehicle detection approach is presented, combining state machines and downhill simplex optimization. In the proposed approach, vehicle detection is modeled as a sequential state transition problem; that is, vehicle arrival, moving, and departure at a chosen detection area. More specifically, the number of bright pixels and their differences, in a chosen area of interest, are calculated and fed into the proposed state machine to detect vehicles. After a vehicle is detected, the location of the headlights is determined using the downhill simplex method. In the proposed optimization process, various headlights were evaluated for possible headlight positions on the detected vehicles; allowing for an optimal headlight position to be located. Simulation results were provided to show the robustness of the proposed approach for nighttime vehicle and headlight detection.

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Methods to Detect Road Features for Video-Based In-Vehicle Navigation Systems

Cited by 24 publications

References 17 publications

Research on Misalignment Detection of Paper Machine Clothing Based on Computer Vision

Research on Misalignment Detection of Paper Machine Clothing Based on Computer Vision

Road intersection detection and classification using hierarchical SVM classifier

State Machine and Downhill Simplex Approach for Vision-Based Nighttime Vehicle Detection

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