Complex lane detection based on dynamic constraint of the double threshold

The quality of detected lane lines has a great influence on the driving decisions of unmanned vehicles. However, during the process of unmanned vehicle driving, the changes in the driving scene cause much trouble for lane detection algorithms. The unclear and occluded lane lines cannot be clearly detected by most existing lane detection models in many complex driving scenes, such as crowded scene, poor light condition, etc. In view of this, we propose a robust lane detection model using vertical spatial features and contextual driving information in complex driving scenes. The more effective use of contextual information and vertical spatial features enables the proposed model more robust detect unclear and occluded lane lines by two designed blocks: feature merging block and information exchange block. The feature merging block can provide increased contextual information to pass to the subsequent network, which enables the network to learn more feature details to help detect unclear lane lines. The information exchange block is a novel block that combines the advantages of spatial convolution and dilated convolution to enhance the process of information transfer between pixels. The addition of spatial information allows the network to better detect occluded lane lines. Experimental results show that our proposed model can detect lane lines more robustly and precisely than state-of-the-art models in a variety of complex driving scenarios.

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Deep reinforcement learning based lane detection and localization

Neurocomputing ◽

10.1016/j.neucom.2020.06.094 ◽

2020 ◽

Vol 413 ◽

pp. 328-338 ◽

Cited By ~ 1

Author(s):

Zhiyuan Zhao ◽

Qi Wang ◽

Xuelong Li

Keyword(s):

Reinforcement Learning ◽

Lane Detection ◽

Detection And Localization

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On-CMOS Image Sensor Processing for Lane Detection

Sensors ◽

10.3390/s21113713 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3713

Author(s):

Soyeon Lee ◽

Bohyeok Jeong ◽

Keunyeol Park ◽

Minkyu Song ◽

Soo Youn Kim

Keyword(s):

Edge Detection ◽

High Speed ◽

Cmos Image Sensor ◽

Image Sensor ◽

Image Resolution ◽

Lane Detection ◽

Frame Rate ◽

Total Power ◽

Detection Accuracy ◽

Total Power Consumption

This paper presents a CMOS image sensor (CIS) with built-in lane detection computing circuits for automotive applications. We propose on-CIS processing with an edge detection mask used in the readout circuit of the conventional CIS structure for high-speed lane detection. Furthermore, the edge detection mask can detect the edges of slanting lanes to improve accuracy. A prototype of the proposed CIS was fabricated using a 110 nm CIS process. It has an image resolution of 160 (H) × 120 (V) and a frame rate of 113, and it occupies an area of 5900 μm × 5240 μm. A comparison of its lane detection accuracy with that of existing edge detection algorithms shows that it achieves an acceptable accuracy. Moreover, the total power consumption of the proposed CIS is 9.7 mW at pixel, analog, and digital supply voltages of 3.3, 3.3, and 1.5 V, respectively.

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Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽

10.3390/electronics10141665 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1665

Author(s):

Jakub Suder ◽

Kacper Podbucki ◽

Tomasz Marciniak ◽

Adam Dąbrowski

Keyword(s):

Edge Detection ◽

Optimal Path ◽

Color Space ◽

Low Complexity ◽

Detection Algorithm ◽

Lane Detection ◽

Detection Methods ◽

Raspberry Pi ◽

Line Detection ◽

Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

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