Fast lane boundary recognition by a parallel image processor

Premachandra, Chinthaka; Gohara, Ryo; Kato, K.

doi:10.1109/smc.2016.7844363

Cited by 8 publications

(8 citation statements)

References 23 publications

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“…This type of method detects lanes very quickly and meets real-time requirements. However, traditional methods are difficult to deal with complex scenes, and curve detection is also difficult [1,2,4,13]. In recent years, with the development of deep learning technology in the field of artificial intelligence, models built using neural networks have been proven to have better performance than traditional methods and can bring better detection results to the model [5,6,14,15].…”

Section: Related Workmentioning

confidence: 99%

“…In traditional methods, parallel processing of fast lane detection has been performed very well, and models based on deep learning can learn from the ideas in traditional methods. For example, Chinthaka Premachandra et al applied parallel processors to image detection and studied a Hough transform suitable for parallel processing to achieve fast lane detection [1]. (3) Amidst challenging conditions like shadows and extreme lighting, the scarcity of adequate visual cues renders lane detection a formidable task.…”

Section: Introductionmentioning

confidence: 99%

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Lane Detection Based on Adaptive Cross-Scale Region of Interest Fusion

Deng,

Liu,

Jiang

et al. 2023

Electronics

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Lane detection, a crucial component of autonomous driving systems, is in charge of precise lane location to ensure that cars navigate lanes appropriately. However, in challenging conditions like shadows and extreme lighting, lanes may become obstructed or blurred, posing a significant challenge to the lane-detection task as the model struggles to extract sufficient visual information from the image. The current anchor-based lane-detection network detects lanes in complex scenes by mapping anchors to images to extract features and calculating the relationship between each anchor and other anchors for feature fusion. However, it is insufficient for anchors to extract subtle features from images, and there is no guarantee that the information carried by each anchor is valid. Therefore, this study proposes the adaptive cross-scale ROI fusion network (ACSNet) to fully extract the features in the image so that the anchor carries more useful information. ACSNet selects important anchors in an adaptive manner and fuses these important anchors with the original anchors across scales. Through this feature extraction method, the features of different field-of-view ranges under complex road surfaces can be learned, and diversified features can be integrated to ensure that lanes can be well detected under complex road surfaces such as shadows and extreme lighting. Furthermore, due to the slender structure of lane lines, there are relatively few useful features in the images. Therefore, this study also proposes a Three-dimensional Coordinate Attention Mechanism (TDCA) to enhance image features. The Three-dimensional Coordinate Attention Mechanism extensively explores relationships among features in the row, column, and spatial dimensions. It calculates feature weights for each of these dimensions and ultimately performs element-wise multiplication with the entire feature map. Experimental results demonstrate that our network achieves excellent performance on the existing public datasets, CULane and Tusimple.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lane Detection Based on Adaptive Cross-Scale Region of Interest Fusion

Deng,

Liu,

Jiang

et al. 2023

Electronics

View full text Add to dashboard Cite

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“…There are many recent studies on compact system development using small, lightweight hardware [32][33][34][35]. In the present study, we used an AEEON UP microcontroller board (Fig.…”

Section: B Microcontroller Boardmentioning

confidence: 99%

“…First, the original image from the camera is cropped by 30% on all sides to remove noise and leave only the central tactile paving in the image. Then, the Hough line transform is applied to find the straight borderlines of the tactile paving [37][38]. Additionally, start and end points of all detected borderlines are determined.…”

Section: Tactile Paving Detectionmentioning

confidence: 99%

Tactile Paving Detection by Dynamic Thresholding Based on HSV Space Analysis for Developing a Walking Support System

et al. 2021

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In this research, we present a system that enables independent walking in individuals with visual impairment by recognizing tactile paving. The system warns the user about dangers posed by deviations from the walking path or possible collisions. Therefore, the tactile paving itself and obstacles on it must be detected. This paper discusses problems related to detecting tactile paving. To address these problems, a new camera-based method is introduced, which detects tactile paving automatically. The method relies on a dynamic threshold approach in the HSV color space, making the image processing more robust against varying lighting conditions, environments, and differences in color. Such an approach is not possible using the methods based on fixed thresholds reported in the literature. The results of our experiments confirm that high rates of tactile paving detection were achieved under various conditions.

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“…Frame and background subtraction can be used to detect moving objects in images [21][22][23][24][25][26][27][28][29][30][31][32]. In frame subtraction, moving objects are detected by calculating the image subtraction between two or a few consecutive frames [21,23,28]. Moving objects must have some minimal speed for detection by frame subtraction.…”

Section: A Moving Object Candidate Extraction By Gaussian Mixture Momentioning

confidence: 99%

Detection and Tracking of Moving Objects at Road Intersections Using a 360-Degree Camera for Driver Assistance and Automated Driving

2020

Self Cite

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Many complicated road intersections are seen while driving. In some, blind spots make it difficult for drivers or automated vehicles to discern moving objects coming from certain directions, possibly confusing drivers or autonomous vehicles wishing to cross or to turn at the intersection. To address this problem, we investigate detection and tracking of all moving objects at an intersection using a single 360degree-view camera (3DVC). Through experiments, we develop methods allowing a 3DVC to capture the entirety of a four-way intersection when installed at one corner. This paper also presents image processing algorithms for detecting and tracking moving objects at intersections by processing images from the installed 3DVC. Experiments under varied conditions demonstrate that the proposed detection algorithm has a very high detection rate. We also confirm the tracking ability for moving objects detected using the proposed algorithm. INDEX TERMS 360-degree camera (Omnidirectional camera), driver assistance, autonomous driving, moving object detection and tracking, image conversion.

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Fast lane boundary recognition by a parallel image processor

Cited by 8 publications

References 23 publications

Lane Detection Based on Adaptive Cross-Scale Region of Interest Fusion

Lane Detection Based on Adaptive Cross-Scale Region of Interest Fusion

Tactile Paving Detection by Dynamic Thresholding Based on HSV Space Analysis for Developing a Walking Support System

Detection and Tracking of Moving Objects at Road Intersections Using a 360-Degree Camera for Driver Assistance and Automated Driving

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