Hybrid Deep Learning Based YOLOv7 Algorithm Animal Aware Intelligent Traffic Control
DOI:
https://doi.org/10.31305/rrijm.2026.v11.n09.006Keywords:
Hybrid Deep Learning YOLOv7 + Deepsort, sensors, Computer Vision, AI powered smart cemras, AI Deep Learning Traffic Control TechnologiesAbstract
In today's era of modern traffic control, road accidents are rapidly increasing, endangering human life. One of the main reasons for this threat to human life is the destruction of nature by humans, such as clearing forests, building factories to suit their own desires, promoting smart cities, etc. As a result, the animals living in the forests are becoming homeless and are roaming the streets. Due to the presence of these animals on the roads, the problem of road accidents is increasing these days due to traffic control. Due to this, the lives of animals are being lost as well as humans. After all, animals are also a part of nature and it is the duty of humans to protect them. To solve this problem, in today's modern times, many technologies have been made the basis through AI to develop intelligent traffic control. Such as thermal imaging cameras, Radar Cameras, Infrared mossion Sensors, AI-powered Smart Cameras, Doppler Radar Sensors, IoT Embedded system and AI computer vision cameras etc. The most powerful technology among these is Hybrid Deep learning-based technology developed by AI. By using YOLOv7 algorithm of YOLO model, it can be used to improve animal awareness and can also increase animal protection by several percent. Due to which human life or animal life can be saved and traffic accidents can be reduced. Computer vision, Deepsort, CCTV, OpenCV techniques are used with Hybrid Deep Learning based YOLOv7. Any type of animal can be identified by analyzing the data obtained from CCTV. Many types of AI based sensors are installed on the road to anticipate accidents even before they occur. These sensors alert humans and animals in advance that some kind of vehicle is about to come on the road, so move away from the vehicle. The typical Hybrid Deep Learning model is very accurate. It reduces the detection time and provides high level performance data.
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