Fast and Robust Traffic Sign Detection

This paper deals with the fast and robust detection of the traffic sign images. A new technique called geometric fragmentation is proposed to detect the red circular traffic signs. It detects the outer ellipses of the signs by combining the left and right fragments of the ellipse objects. A search based on the geometric fragmentation is used to find the ellipse fragments. This search is somewhat similar to genetic algorithm (GA) in the sense that it employs the terms of individual, population, crossover, and objective function usually used in GA. To increase the accuracy and reduce the computational time, a new objective function is introduced for evaluating the individuals. The algorithm was tested for detecting the red circular traffic signs from the real scene image. The experimental results show that the proposed algorithm has a higher detection rate with a lower computational cost compared with the referential genetic algorithm-based ellipse detection

Adversarial Attack On Yolov5 For Traffic And Road Sign Detection

This paper implements and investigates popular adversarial attacks on the YOLOv5 Object Detection algorithm. The paper explores the vulnerability of the YOLOv5 to adversarial attacks in the context of traffic and road sign detection. The paper investigates the impact of different types of attacks, including the Limited memory Broyden Fletcher Goldfarb Shanno (L-BFGS), the Fast Gradient Sign Method (FGSM) attack, the Carlini and Wagner (C&W) attack, the Basic Iterative Method (BIM) attack, the Projected Gradient Descent (PGD) attack, One Pixel Attack, and the Universal Adversarial Perturbations attack on the accuracy of YOLOv5 in detecting traffic and road signs. The results show that YOLOv5 is susceptible to these attacks, with misclassification rates increasing as the magnitude of the perturbations increases. We also explain the results using saliency maps. The findings of this paper have important implications for the safety and reliability of object detection algorithms used in traffic and transportation systems, highlighting the need for more robust and secure models to ensure their effectiveness in real-world applications

Stereoscopic vision in vehicle navigation.

Traffic sign (TS) detection and tracking is one of the main tasks of an autonomous vehicle which is addressed in the field of computer vision. An autonomous vehicle must have vision based recognition of the road to follow the rules like every other vehicle on the road. Besides, TS detection and tracking can be used to give feedbacks to the driver. This can significantly increase safety in making driving decisions. For a successful TS detection and tracking changes in weather and lighting conditions should be considered. Also, the camera is in motion, which results in image distortion and motion blur. In this work a fast and robust method is proposed for tracking the stop signs in videos taken with stereoscopic cameras that are mounted on the car. Using camera parameters and the detected sign, the distance between the stop sign and the vehicle is calculated. This calculated distance can be widely used in building visual driver-assistance systems

Cooperative Road Sign and Traffic Light Using Near Infrared Identification and Zigbee Smartdust Technologies

Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I as well as I2V)applications are developing very fast. They rely on telecommunication and localizationtechnologies to detect, identify and geo-localize the sources of information (such as vehicles,roadside objects, or pedestrians). This paper presents an original approach on how twodifferent technologies (a near infrared identification sensor and a Zigbee smartdust sensor)can work together in order to create an improved system. After an introduction of these twosensors, two concrete applications will be presented: a road sign detection application and acooperative traffic light application. These applications show how the coupling of the twosensors enables robust detection and how they complement each other to add dynamicinformation to road-side objects

Real time implementation of SURF algorithm on FPGA platform

Too many traffic accidents are caused by drivers’ failure of noticing buildings, traffic sign and other objects. Video based scene or object detection which can easily enhance drivers’ judgment performance by automatically detecting scene and signs. Two of the recent popular video detection algorithms are Background Differentiation and Feature based object detection. The background Differentiation is an efficient and fast way of observing a moving object in a relatively stationary background, which makes it easy to be implemented on a mobile platform and performs a swift processing speed. The Feature based scene detection such like the Speeded Up Robust Feature (SURF), is an appropriate way of detecting specific scene with accuracy and rotation and illumination invariance. By comparison, SURF computational expense is much higher, which remains the algorithm limited in real time mobile platform. In this thesis, I present two real time tracking algorithms, Differentiation based and SURF based scene detection systems on FPGA platform. The proposed hardware designs are able to process video of 800*600 resolution at 60 frames per second, the video clock rate is 40 MHz

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

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