Real Time IoT Based Camel-Vehicle Collision Avoidance System for KSA

Saad, Waleed; Alsayyari, Abdulaziz

doi:10.1109/iccais48893.2020.9096714

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…One proposal involves using an omni-directional radar that detects movement and uploads it to the cloud for analysis. If the speed of movement falls within a specific range, alarms will go off [6]. Another proposal involves implanting chips in camels that can be tracked through LoRaWAN and GPS.…”

Section: Related Workmentioning

confidence: 99%

Spot-the-Camel: Computer Vision for Safer Roads

AlNujaidi¹,

AlHabib²,

Alodhieb³

2023

IJAIA

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As the population grows and more land is being used for urbanization, ecosystems are disrupted by our roads and cars. This expansion of infrastructure cuts through wildlife territories, leading to many instances of Wildlife-Vehicle Collision (WVC). These instances of WVC are a global issue that is having a global socio-economic impact, resulting in billions of dollars in property damage and, at times, fatalitiesfor vehicle occupants. In Saudi Arabia, this issue is similar, with instances of Camel-Vehicle Collision (CVC) being particularly deadly due to the large size of camels, which results in a 25% fatality rate [1].The focus of this work is to test different object detection models on the task of detecting camels on theroad. The Deep Learning (DL) object detection models used in the experiments are: CenterNet, Efficient Det, Faster R-CNN, SSD, and YOLOv8. Results of the experiments show that YOLOv8 performed the best in terms of accuracy and was the most efficient in training. In the future, the plan is to expand on this work by developing a system to make countryside roads safer.

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Section: Related Workmentioning

confidence: 99%

Spot-the-Camel: Computer Vision for Safer Roads

AlNujaidi¹,

AlHabib²,

Alodhieb³

2023

IJAIA

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ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management

Nikhilesh Krishna,

Rauniyar,

Kireeti Sai Bharadwaj

et al. 2024

Lecture Notes in Networks and Systems

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Protall (An Intelligent, multi-sensor, comprehensive obstacle avoidance system for automobiles and UAVs)

Dhelia¹,

Chaughule²,

Choraria³

et al. 2022

J. Phys.: Conf. Ser.

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In this era of Artificial Intelligence and Automation, manufacturing and testing of self-driving cars and autonomous delivery of parcels to the desired location with the help of UAVs have a considerable amount of growth in the industry. This advancement in technology also raises safety issues due to the failure of sensors to detect the object or sometimes because of the dynamic environment. Protall (Protect-all) is an integrated solution for UAV and automobile vehicles to provide ultimate safety to both itself and its surroundings. With the strategic sensor integration and its intelligent processing, it aims to produce controlled output and hence, ensures to prevent any possible failures from occurring. The system constantly reacts to the environment and maintains comprehensive interaction with the user thus, enabling it to handle any dynamic situation and hence, it emerges as an optimal solution for Vehicles and UAVs.

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Real Time IoT Based Camel-Vehicle Collision Avoidance System for KSA

Cited by 6 publications

References 17 publications

Spot-the-Camel: Computer Vision for Safer Roads

Spot-the-Camel: Computer Vision for Safer Roads

ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management

Protall (An Intelligent, multi-sensor, comprehensive obstacle avoidance system for automobiles and UAVs)

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