An Improved YoLov3 Framework For Real-Time Object Detection In Autonomous Vehicles

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Optical vision is a crucial part of self-driving cars. Vehicle, street, building, pedestrian, and road sign detection that is accurate could help self-driving cars drive as safely as human beings. Object detection, on the other hand, has been a tough task for years due to the effects of lighting, topology, and occlusion on photographs of objects in the real world. Many Convolutional Neural Networks (CNN) based algorithms have improved the detection performance in larger samples in recent years. These approaches have sluggish identification speed, on the other hand, limit their use in real-time scenarios. The Resnet50, revolutionary network architecture with exceptional efficiency acted as the backbone network and the addition of CAR (Concatenation and residual) architecture in the YOLO model is proposed in this research. This architecture uses a topology similar to that of a residual network, but with a few more layers and a few skip connections between layers for which feature reuse is not necessary. This enhanced network is used to detect real-time objects in the You Look Only Once version 3 (YOLO V3) detector. COCO, ImageNet, and PASCAL VOC databases as well as custom datasets are used to train this network for Indian Vehicles. The proposed network outperforms state-of-the-art CNNs, according to the results of the experiments.