A Novel Genetically Optimized Convolutional Neural Network for Traffic Sign Recognition: A New Benchmark on Belgium and Chinese Traffic Sign Datasets

Jain, Arpan; Mishra, Apoorva; Shukla, Anupam; Tiwari, Ritu

doi:10.1007/s11063-019-09991-x

Cited by 29 publications

(13 citation statements)

References 35 publications

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“…• BTSC [28]: Belgium traffic signs with 62 classes, divided into 7000 images for training and testing. • CIFAR-10 [29]: ten general classes (e.g., dog, car ...), with 50000 instances for training and 10000 for testing.…”

Section: A Data Profilementioning

confidence: 99%

Benchmarking Safety Monitors for Image Classifiers with Machine Learning

Ferreira

Arlat

Guiochet

et al. 2021

2021 IEEE 26th Pacific Rim International Symposium on Dependable Computing (PRDC)

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High-accurate machine learning (ML) image classifiers cannot guarantee that they will not fail at operation. Thus, their deployment in safety-critical applications such as autonomous vehicles is still an open issue. The use of fault tolerance mechanisms such as safety monitors is a promising direction to keep the system in a safe state despite errors of the ML classifier. As the prediction from the ML is the core information directly impacting safety, many works are focusing on monitoring the ML model itself. Checking the efficiency of such monitors in the context of safety-critical applications is thus a significant challenge. Therefore, this paper aims at establishing a baseline framework for benchmarking monitors for ML image classifiers. Furthermore, we propose a framework covering the entire pipeline, from data generation to evaluation. Our approach measures monitor performance with a broader set of metrics than usually proposed in the literature. Moreover, we benchmark three different monitor approaches in 79 benchmark datasets containing five categories of out-of-distribution data for image classifiers: class novelty, noise, anomalies, distributional shifts, and adversarial attacks. Our results indicate that these monitors are no more accurate than a random monitor. We also release the code of all experiments for reproducibility.

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Section: A Data Profilementioning

confidence: 99%

Benchmarking Safety Monitors for Image Classifiers with Machine Learning

Ferreira

Arlat

Guiochet

et al. 2021

2021 IEEE 26th Pacific Rim International Symposium on Dependable Computing (PRDC)

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“…Given that the 3D point clouds are spatio-temporally synchronized with 2D images in a MLS system, it is straightforward to extract images of the traffic sign panels and perform computer vision processes on them to extract semantic information: Some works rely on machine learning strategies such as Support Vector Machines using custom descriptors [100] or existing features such as a Histogram of Oriented Gradients (HOG) [106], while others rely on the more recent trend of Deep Learning-approaching an end-to-end recognition process using Deep Bolztmann Machines [104,105] or convolutional neural networks [107] (Figure 5b). These techniques also are employed using only imagery data [108,109]. All the mentioned work is summarized in Table 4.…”

Section: Traffic Signsmentioning

confidence: 99%

Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring

et al. 2019

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Improving the resilience of infrastructures is key to reduce their risk vulnerability and mitigate impact from hazards at different levels (e.g., from increasing extreme events, driven by climate change); or from human-made events such as: accidents, vandalism or terrorist actions. One of the most relevant aspects of resilience is preparation. This is directly related to: (i) the risk prediction capability; (ii) the infrastructure monitoring; and (iii) the systems contributing to anticipate, prevent and prepare the infrastructure for potential damage. This work focuses on those methods and technologies that contribute to more efficient and automated infrastructure monitoring. Therefore, a review that summarizes the state of the art of LiDAR (Light Detection And Ranging)-based data processing is presented, giving a special emphasis to road and railway infrastructure. The most relevant applications related to monitoring and inventory transport infrastructures are discussed. Furthermore, different commercial LiDAR-based terrestrial systems are described and compared to offer a broad scope of the available sensors and tools to remote monitoring infrastructures based on terrestrial systems.

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“…On BTSC dataset there are 3 methods include Residual convolutional blocks [58], Single CNN with 3 STNs [56], and VGG-16 [61] which based on transfer learning using VGG-16 model where Genetic Algorithm (GA) is used for discovering the optimal parameters of epochs number and the learning rate value. In table 3 recognition accuracy evaluation between reported results and the proposed method are illustrated.…”

Section: B Analysis On Btsc Datasetmentioning

confidence: 99%

A Novel Method for Traffic Sign Recognition Based on DCGAN and MLP With PILAE Algorithm

Mahmoud

Guo

2019

IEEE Access

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This paper centers on a novel method for traffic sign recognition (TSR). The method comprises of two major steps: 1) make strong representations for TSR images, by extraction deep features with the deep convolutional generative adversarial networks (DCGANs) and 2) classifier defined by multilayer perceptron (MLP) neural networks trained with a pseudoinverse learning autoencoder (PILAE) algorithm. The PILAE training process is considered efficient in which it does not require the number of hidden layers specified nor does it need the setting of the learning control parameters. This results in the PILAE classifier attaining a better performance in terms of both accuracy and efficiency. Empirical results from the German TSR (GTSRB) and Belgium traffic sign classification (BTSC) have proved that TSR achieves excellent results with other algorithms and reasonably low complexity. INDEX TERMSDeep convolutional generative adversarial networks (DCGAN), feature extraction, pseudoinverse learning autoencoder (PILAE), traffic sign recognition (TSR).

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A Novel Genetically Optimized Convolutional Neural Network for Traffic Sign Recognition: A New Benchmark on Belgium and Chinese Traffic Sign Datasets

Cited by 29 publications

References 35 publications

Benchmarking Safety Monitors for Image Classifiers with Machine Learning

Benchmarking Safety Monitors for Image Classifiers with Machine Learning

Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring

A Novel Method for Traffic Sign Recognition Based on DCGAN and MLP With PILAE Algorithm

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