A Multistage Framework for Detection of Very Small Objects

Small object detection is one of the most challenging problems in computer vision. Algorithms based on state-of-the-art object detection methods such as R-CNN, SSD, FPN, and YOLO fail to detect objects of very small sizes. In this study, we propose a novel method to detect very small objects, smaller than 8×8 pixels, that appear in a complex background. The proposed method is a multistage framework consisting of an unsupervised algorithm and three separately trained supervised algorithms. The unsupervised algorithm extracts ROIs from a high-resolution image. Then the ROIs are upsampled using SRGAN, and the enhanced ROIs are detected by our two-stage cascade classifier based on two ResNet50 models. The maximum size of the images used for training the proposed framework is 32×32 pixels. The experiments are conducted using rescaled German Traffic Sign Recognition Benchmark dataset (GTSRB) and downsampled German Traffic Sign Detection Benchmark dataset (GTSDB). Unlike MS COCO and DOTA datasets, the resulting GTSDB turns out to be very challenging for any small object detection algorithm due to not only the size of objects of interest but also the complex textures of the background. Our experimental results show that the proposed method detects small traffic signs with an average precision of 0.332 at the intersection over union of 0.3

GR-210 - Detection of Small Traffic Signs Using Image Super Resolution

In this study, we propose a novel method to detect small traffic signs that appeared in dashboard camera images. Our method is a framework consisting of the following three distinct algorithms. Grouping Window, Super Resolution Generative Adversarial Network (SRGAN), and a two-stage cascade classifier. Potential regions of interest (ROI) are extracted with Grouping Window which is a sophisticated modification of the traditional sliding window technique. The ROI are upsampled and enhanced using SRGAN. Then the traffic signs among high-resolution ROI are detected and identified by the two-stage cascade classifier of which the first stage filters the ROI that do not contain traffic signs and the second stage classifies the ROI that contains traffic signs into respective classes. The proposed method is capable of detecting traffic signs in the 5-8 square-pixel range. The detection of small objects in this square-pixel range is not generally addressed by state-of-the-art frameworks such as R-CNN and YOLO. We trained our method by using the German Traffic Sign Recognition Benchmark dataset (GTSRB) and tested it on random dashboard camera images containing small traffic signs. The experimental results on 15 random dashboard camera images show that our baseline model localizes 10 of the 23 small traffic signs belonging to the aforementioned pixel range and produces 70 false positives in total. Also, it classifies only one of the detected traffic signs correctly into 43 classes. We plan to improve our method by using image denoising techniques and comparing results

VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection

Although traffic sign detection has been studied for years and great progress has been made with the rise of deep learning technique, there are still many problems remaining to be addressed. For complicated real-world traffic scenes, there are two main challenges. Firstly, traffic signs are usually small size objects, which makes it more difficult to detect than large ones; Secondly, it is hard to distinguish false targets which resemble real traffic signs in complex street scenes without context information. To handle these problems, we propose a novel end-to-end deep learning method for traffic sign detection in complex environments. Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for the small size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance. To comprehensively evaluate the proposed method, we do experiments on several traffic sign datasets as well as the general object detection dataset and the results have shown the effectiveness of our proposed method

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