Traffic Sign Detection
13 papers with code • 3 benchmarks • 5 datasets
Convolutional neural networks are the most widely used deep learning algorithms for traffic signal classification till date but they fail to capture pose, view, orientation of the images because of the intrinsic inability of max pooling layer. This paper proposes a novel method for Traffic sign detection using deep learning architecture called capsule networks that achieves outstanding performance on the German traffic sign dataset. Capsule network consists of capsules which are a group of neurons representing the instantiating parameters of an object like the pose and orientation by using the dynamic routing and route by agreement algorithms. unlike the previous approaches of manual feature extraction, multiple deep neural networks with many parameters, our method eliminates the manual effort and provides resistance to the spatial variances. CNNs can be fooled easily using various adversary attacks and capsule networks can overcome such attacks from the intruders and can offer more reliability in traffic sign detection for autonomous vehicles. Capsule network have achieved the state-of-the-art accuracy of 97. 6% on German Traffic Sign Recognition Benchmark dataset (GTSRB).
Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions
Experimental results show that benchmarked algorithms are highly sensitive to tested challenging conditions, which result in an average performance drop of 0. 17 in terms of precision and a performance drop of 0. 28 in recall under severe conditions.
In this work, we present NEO, a model agnostic framework to detect and mitigate such backdoor attacks in image classification ML models.
Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics
We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics.
A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
The root cause of this issue is that no public dataset contains both traffic light and sign labels, which leads to difficulties in developing a joint detection framework.
Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]
Robust and reliable traffic sign detection is necessary to bring autonomous vehicles onto our roads.
Traffic sign detection systems constitute a key component in trending real-world applications, such as autonomous driving, and driver safety and assistance.
Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory.
Deep learning has been successfully applied to several problems related to autonomous driving.