Implementation of YOLOv4 Algorithm for Multiple Object Detection in Image and Video Dataset using Deep Learning and Artificial Intelligence for Urban Traffic Video Surveillance Application

Artificial intelligence and machine learning have great ability to solve real-time problems related to various fields and have many applications. One such subfield is deep learning, deep learning has many architectures like DNN, CNN and RNN. One of the major applications of deep learning is object detection. YOLO a SOTA object detector considered to be a smart convolutional neural network having capability of detecting objects in the image, classifying them accordingly and localizing the object perfectly with annotations. YOLOv4 is employed in this work for multiple object detection in image and video for traffic surveillance applications trained using a custom dataset created with Indian road traffic images. 9 different classes namely Car, Bus, Van, Truck, Two-wheeler, Auto, Person, Bicycle and Mini truck are considered in creating custom dataset. Proposed custom model trained for 500 epochs with a custom image dataset has achieved a training mAP of 92% and a training loss of 0.001. Achieved an accuracy of 99.28%, sensitivity of 94.44% and MCC of 96.78% on unseen or test dataset. Proposed model predicted all the classes with an average precision of 98.32%. This proves that our model is robust and proficient in detecting road traffic classes and has scope in developing traffic surveillance systems.