VGG16 & YOLO Object Detection for Cars

In “Object Detection Utilizing AI,” I introduced object detection as the combined task of localizing and classifying multiple objects in images and video, highlighting applications in autonomous vehicles, surveillance, and medical imaging, as well as key challenges in accuracy and efficiency. I explained core techniques such as sliding windows and image cropping and detailed a project pipeline that classified image patches as car, truck, or other, progressing from a simple perceptron (~72% accuracy) to a convolutional neural network (~82%) and finally to transfer learning with VGG16 (~95%). I also discussed YOLO as a fast single-stage detection alternative and concluded by emphasizing object detection’s growing impact on automotive safety and intelligent transportation systems.