![]() More details of the project can be found here. In this post, I would like to introduce a robust tool for synthesizing hazy/foggy image data from clean images using Monodepth and Atmospheric Scattering Model. Or another unified system called MultiScale Domain Adaptive YOLO (MS-DAYOLO) has been proposed which is integrated with the state-of-the-art one-stage object detection model YOLOv4 and a Domain Adaptive Network (DAN) for domain invariant feature learning.įor such studies, we need to prepare a diverse dataset that can help to train a powerful object detection model to tackle the domain shift issue. The difference in performance between a hazy input (left) and a haze-free input (right) (red: ground truth, green: detection) (Image by Author)Ī simple but effective solution is applying an independent environmental condition classifier along with the object detector to recognize the environmental changes as presented in this paper: Enhancement of Robustness in Object Detection Module for Advanced Driver Assistance Systems ( read full paper). From the figure, we can see clearly the effect of domain shift on the detector’s performance. The figure below shows a comparison in performance between a hazy input (left) and a haze-free input (right) when applying a pre-trained YOLOv4 model which is trained on original clean data (red: ground truth, green: detection). Hence, to bring more proficient performance, object detectors should be more robust against domain shift problems. For instance, the object detector of an autonomous vehicle is trained to work well on favorable training data which is captured in good weather conditions while the factual weather during the deployment phase can include rain or fog. One of the most popular reasons for that unexpected incident is because domain shift problem.ĭomain shift happens when the distribution of testing data is different from that of training data. The performances of object detectors in practice usually drop despite being trained well with almost 99.99% accuracy during offline training. However, there still exists a huge gap between academic research and realistic deployment. They've delayed the game for an entire six extra months so this is honestly inexcusable. ![]() The common complaints I've read are about the awful story, outdated gameplay, and constant glitches and Volition definitely deserve to be called out for it. Various object detectors such as R-CNN family (R-CNN, Fast R-CNN, Faster R-CNN, Cascade R-CNN), YOLO series (YOLOv1-v4) have been proposed, and abundant driving object detection datasets including BDD100K, WAYMO, etc. This game is getting annihilated with poor review scores ranging around the mid sixties. The recent era of deep learning and computer vision leads to the rapid development of autonomous driving technology where object detection plays an extremely important role.
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