Efficient Object Detection on Sparse-to-Dense Depth Prediction

Detecting objects has been an important and intricate field for many years, particularly in the safety and healthcare sectors as well as the automotive industry. Much research on identifying objects has been shown to be a challenging one. When compared to other existing algorithms, You Only Look Once (YOLO) provides superior power and speed for this task. Semantic segmentation is a computer vision challenge in which the different parts of an image are sorted into classes that can be understood based on their meaning. When it is said that a class is semantically subject to interpretation, it means that it has some significance in the real world. The scope of the work is to detect the objects in an image and draw a bounding box around the object with its class and confidence score. The experimental results show that the objects are detected, classified, and localized with their confidence score.