Detection of Lanes and Objects Using Deep Learning Techniques

Sai, Paila Girish; Chandu, Sunkollu Sai; Aishwarya, Sogani Chandrika; Revanth, Velagapudi; Geetha, G.

doi:10.1109/icirca54612.2022.9985664

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Real-time data transmission to control terminals through vision and deep learning algorithms facilitated the issuance of precise control commands that enable the vehicle to reliably and safely arrive at its destination [18]. Sai et al explored the use of CNN combined with Open CV and the You Only Look Once (YOLO) algorithm to detect moving objects in the lane so that self-driving vehicles can clearly master environmental conditions, enabling stable and secure lane navigation [19]. Yogitha et al introduced the use of CNN and the sparse structure learning algorithm (SSLA) to analyze the images detected by self-driving vehicle sensors.…”

Section: Introductionmentioning

confidence: 99%

Applying a Novel Image Recognition Curve-Fitting Control Strategy Combined with a Cloud Monitoring Technique into an Electric Self-Driving Vehicle (ESDV) to Improve Its Operation Efficiency

Liu,

Lin,

Lai

et al. 2023

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This study aims to develop an image recognition curve-fitting (IRCF) control strategy integrated with a cloud monitoring technique for application in electric self-driving vehicles (ESDVs) to improve their operation efficiency. The study focuses on an electric vehicle designed to reduce the carbon emissions and promote sustainability. The main camera, combined with the IRCF control strategy, was used to control the ESDV to enhance its operational efficiency. The proposed ESDV employs a pair of cameras to capture images and transmit them to the cloud-based web monitoring platform in real time. This allows the researchers to adjust the control parameters and promptly remove the road obstacles. The ESDV is equipped with a horn, two ultrasonic sensors, and an LED display, which can instantly detect the obstacles ahead of and behind the vehicle. When there are obstacles on the road, the vehicle will automatically stop, and the LED display will provide a visual representation of the obstacles, accompanied by the sounding of the horn as a warning signal. Meanwhile, the secondary camera detects the signal mark and feeds it back to the LED display, thereby informing passengers and other road users about the prevailing driving conditions. The proposed IRCF control strategy was compared with the traditional Hough line detection method on a 110 m ring road. The results revealed that the proposed control strategy outperformed the traditional Hough line detection method in terms of speed, efficiency, and running dexterity. Therefore, integrating the proposed control strategy into the automatic assistance driving system can improve the ESDV’s operation efficiency. Furthermore, the combination of the obstacle detection and signal sign detection functions for the ESDV used in this study can better fulfill the actual ESDV operation requirements on the road.

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Section: Introductionmentioning

confidence: 99%