Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

MFL-YOLO: An Object Detection Model for Damaged Traffic Signs

Traffic signs are important facilities to ensure traffic safety and smooth flow, but may be damaged due to many reasons, which poses a great safety hazard. Therefore, it is important to study a method to detect damaged traffic signs. Existing object detection techniques for damaged traffic signs are still absent. Since damaged traffic signs are closer in appearance to normal ones, it is difficult to capture the detailed local damage features of damaged traffic signs using traditional object detection methods. In this paper, we propose an improved object detection method based on YOLOv5s, namely MFL-YOLO (Mutual Feature Levels Loss enhanced YOLO). We designed a simple cross-level loss function so that each level of the model has its own role, which is beneficial for the model to be able to learn more diverse features and improve the fine granularity. The method can be applied as a plug-and-play module and it does not increase the structural complexity or the computational complexity while improving the accuracy. We also replaced the traditional convolution and CSP with the GSConv and VoVGSCSP in the neck of YOLOv5s to reduce the scale and computational complexity. Compared with YOLOv5s, our MFL-YOLO improves 4.3 and 5.1 in F1 scores and mAP, while reducing the FLOPs by 8.9%. The Grad-CAM heat map visualization shows that our model can better focus on the local details of the damaged traffic signs. In addition, we also conducted experiments on CCTSDB2021 and TT100K to further validate the generalization of our model.Comment: 11 pages, 8 figures, 4 table

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin