A Survey on Real-Time Object Detection Algorithms

One of the major technological advances that aims to more accurately perceive the real world through digital images and videos is computer vision. Amongst the many innovative breakthroughs in this field of study is object detection, a branch of computer vision. This methodology finds instances of specific objects in visual content. The terms object recognition, object identification, and image detection are all mutually inclusive for object detection. By identifying objects in visual images or videos, it enables information systems to "view" their surroundings. This paper compares and contrasts the most extensively used methodologies and approaches to achieving object detection in real time. Faster R-CNN, SSD, and YOLO are three algorithms used in this cultural shift that simplify the process for programmers to identify an instance in an image. The processes that enable the model's parameters to be trained, calculated, approximated, and quantified are compared with the conventional methodologies. The convenience and limitations of each of these techniques are further discussed, analyzed, and tabulated.