Obstacle detection &amp;amp; elimination of shadows for an image processing based automated vehicle

Prashanth, C. R.; Sagar, T.; Bhat, Naresh; Naveen, D.; Rupanagudi, Sudhir Rao; Kumar, Rahul

doi:10.1109/icacci.2013.6637199

Cited by 19 publications

(5 citation statements)

References 7 publications

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“…In this space, the color tone of an object is solely defined by the Hue value, with 0 representing white and 255 representing black. Therefore, the image is converted to the HSV system [37,38]. This is illustrated in Figure 7a.…”

Section: Color Detector With Opencvmentioning

confidence: 99%

Low-Cost Computer-Vision-Based Embedded Systems for UAVs

Ortega,

Loyaga,

Cruz

et al. 2023

Robotics

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Unmanned Aerial Vehicles (UAVs) are versatile, adapting hardware and software for research. They are vital for remote monitoring, especially in challenging settings such as volcano observation with limited access. In response, economical computer vision systems provide a remedy by processing data, boosting UAV autonomy, and assisting in maneuvering. Through the application of these technologies, researchers can effectively monitor remote areas, thus improving surveillance capabilities. Moreover, flight controllers employ onboard tools to gather data, further enhancing UAV navigation during surveillance tasks. For energy efficiency and comprehensive coverage, this paper introduces a budget-friendly prototype aiding UAV navigation, minimizing effects on endurance. The prototype prioritizes improved maneuvering via the integrated landing and obstacle avoidance system (LOAS). Employing open-source software and MAVLink communication, these systems underwent testing on a Pixhawk-equipped quadcopter. Programmed on a Raspberry Pi onboard computer, the prototype includes a distance sensor and basic camera to meet low computational and weight demands.Tests occurred in controlled environments, with systems performing well in 90% of cases. The Pixhawk and Raspberry Pi documented quad actions during evasive and landing maneuvers. Results prove the prototype’s efficacy in refining UAV navigation. Integrating this cost-effective, energy-efficient model holds promise for long-term mission enhancement—cutting costs, expanding terrain coverage, and boosting surveillance capabilities.

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Section: Color Detector With Opencvmentioning

confidence: 99%

Low-Cost Computer-Vision-Based Embedded Systems for UAVs

Ortega,

Loyaga,

Cruz

et al. 2023

Robotics

View full text Add to dashboard Cite

show abstract

“…Some approaches depends on using multiple images, while some use filters on the images or by the use of small-baselines which is also termed as micro-baseline. Prashanth et al, [1] have used web camera with no auxiliary sensors. A novel segmentation algorithm was used to detect the object which has same color as that of the road by eliminating shadows.…”

Section: Literature Reviewmentioning

confidence: 99%

Object Detection and Distance Estimation of a Mobile Robot with Stereo Vision

Guin¹

2018

Helix

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In day to day life, the studies on robotics are increasing very rapidly. Especially, for obstacle detection and estimating distance in dynamic and static environments. Therefore, researchers contribute more of their interest towards robotics as well as artificial intelligence. Hence, this paper mainly focused on detecting object and how we are estimating distance of a mobile robot. There are many solutions already given by the numerous researchers such as laser sensor, sonar sensor or camera sensor for estimating distance process. The main goal of our proposed approach is to fuse information gained from the stereo vision, infrared and ultrasonic sensors to enhance the accuracy. Thus, we proposed a system with a hybrid approach system.

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“…The controller is being helped by the ultrasonic sensor that empowers the deterrent location capacity in the bot. Prashanth C R et al [7] proposed a novel yet basic calculation using picture preparing systems. The reenactment of the calculation was performed utilizing the Simulink instrument in MATLAB, rendition 2012a.…”

Section: Related Workmentioning

confidence: 99%

Obstacle Detection for Vehicles Using Sensors and Artificial Intelligence

Bhatt¹

2017

ijarcs

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Obstacle distinguishing proof is one of the rule parts of the control system and course of self-overseeing vehicles. With the snappy headway of clever robot advancement, Obstacle recognizable proof in the dark environment is a key development of astute transportation and self-decision robot course. Obstacle distinguishing proof and avoidance is minimal level of capacity, which suggests that it is the foundation of other bigger measure of capacities completing safely. This paper concentrates on related work accomplished for recognition of articles, which can be helpful to identify snags coming in the method for vehicles with the goal that mishaps can be forestalled. We give survey of different obstruction identification methods with their confinements that are proposed by different analysts as of late.

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Obstacle detection & elimination of shadows for an image processing based automated vehicle

Cited by 19 publications

References 7 publications

Low-Cost Computer-Vision-Based Embedded Systems for UAVs

Low-Cost Computer-Vision-Based Embedded Systems for UAVs

Object Detection and Distance Estimation of a Mobile Robot with Stereo Vision

Obstacle Detection for Vehicles Using Sensors and Artificial Intelligence

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