Animal Detection and Collision Avoidance Using IOT Based Deep Learning
Animal Detection and Collision Avoidance Using IOT Based Deep Learning
One of the main causes of traffic accidents is still animals unexpectedly crossing the road. Animals in highways can be found using the Animal Detection System (ADS), a computer vision-based technology. For precise real-time animal species identification, the system uses the YOLOv3 algorithm. The ADS can analyse individual photos and find animals there using the pretrained model. The Yolov3 algorithm is used in our system to determine whether the input matches the animal-based pretrained model. Real-time animal detection is intended, and when an animal is spotted nearby, the motor is supposed to instantly stop to safeguard everyone's safety—both people and animals. The suggested system is made up of a camera mounted on a moving platform, such a vehicle, and a motor control.
Deep learning, YOLOv3(You Only Look Once), internet of things, arduino , dc motor
Deep learning, YOLOv3(You Only Look Once), internet of things, arduino , dc motor
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