Object detection has become the hottest topic in deep learning and pattern recognition research for the last few years and has been the must-known problem for all computer vision researchers. If you are reading this post because you know what is the interesting thing in the post title, I believe you got some background in object detection, so I would like to ignore explaining the fundamental things like what is object detection and how many types of object detectors, the answers can be found easily in millions of sources obtained by typing very simple keywords on Google or whatever searching sites. But at least, I can start by summarizing the series of YOLO algorithms that have been the icon of object detection so far and is the most attractive baseline method that other approaches are improved based on.
The first version of YOLO was introduced by Joseph Redmon and his co-authors in 2015 which made a breakthrough in real-time object detection. YOLOv1 is a one-stage object detector with fast inference speed and acceptable accuracy compared with two-stage methods at that time. YOLOv2, also referred to as YOLO9000, was proposed one year later to improve the detection accuracy by applying the concept of anchor box. In 2016, further improvements were provided in YOLOv3 with a new backbone network Darknet53 and the capability of detecting objects at three different scales using Feature Pyramid Network (FPN) as the model neck. From the next version, YOLOv4, Joseph announced that he stopped going on this project due to some individual reasons and gave the leading privilege of YOLO project to Alexey Bochkovskiy, and Alexey introduced YOLOv4 in 2020. YOLOv4 has improved the performance of the predecessor YOLOv3 by using a new backbone, CSPDarknet53 (CSP stands for Cross Stage Partial), adding Spatial Pyramid Pooling (SPP), Path Aggregation Network (PAN), and introducing mosaic data augmentation method. You can have a look at YOLO project via the official website or the github repo darknet.
Currently, YOLOv4 has been the state-of-the-art model in the series of YOLO (there actually exists a version named YOLOv5, however, this version has not been confirmed as an official version due to some reasons, which can be found in this article). However, YOLOv4 is still not optimized for all scenarios; that is, in the case of the scene that has numerous small objects, YOLOv4 is still getting…
Continue reading: https://towardsdatascience.com/yolov4-5d-an-enhancement-of-yolov4-for-autonomous-driving-2827a566be4a?source=rss—-7f60cf5620c9—4