A deep learning approach to traffic lights: Detection, tracking, and classification

Behrendt, Karsten; Libor, Novák; Botros, Rami

doi:10.1109/icra.2017.7989163

Cited by 195 publications

(129 citation statements)

References 16 publications

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“…By disregarding these instances through visual inspection of the detected cases, the precision would increase to 94.66% and, as a consequence, the F1score would achieve 0.933. The full list of images under this condition is available here 3 . In addition to these cases, a substantial amount of false positives (50.86%) include other elements of the driving domain, such as rear-view mirrors, other types of signs, and headlights.…”

Section: Resultsmentioning

confidence: 99%

“…The success of deep learning applications on autonomous driving and advanced driver assistance systems (ADAS) is unequivocal. For instance, DNNs have been used in scene semantic segmentation [2], traffic light detection [3], crosswalk classification [4], [5], traffic sign detection [6], pedestrian analysis [7], car heading direction estimation [8] and many other applications. In this work, we focus on the traffic sign detection problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Tabelini

Paixão

Berriel

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) for proper training. The acquisition of such real-world data sets is not always possible in the autonomous driving context, and sometimes their annotation is not feasible (e.g., takes too long or is too expensive). Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. It turns out that traffic sign detection is a problem in which these three issues are seen altogether. In this work, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the domain of interest, and (ii) templates of the traffic signs, i.e., templates synthetically created to illustrate the appearance of the category of a traffic sign. The effortlessly generated training database is shown to be effective for the training of a deep detector (such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94%, 91% and 93%, respectively. The experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background, which is in the opposite direction of the common sense for deep learning.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Tabelini

Paixão

Berriel

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

show abstract

“…1). Deep learning approaches have set the benchmark on many popular object detection datasets, such as PASCAL VOC [17] and COCO [18], and have been widely applied in autonomous driving, including detecting traffic lights [19]- [22], road signs [23]- [25], people [26]- [28], or vehicles [29]- [33], to name a few. State-of-the-art deep object detection networks follow one of two approaches: the two-stage or the one-stage object detection pipelines.…”

Section: Deep Object Detectionmentioning

confidence: 99%

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

Feng

Haase-Schütz

Rosenbaum

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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307

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Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of "what to fuse", "when to fuse", and "how to fuse" remain open. This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving. To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research. We then summarize the fusion methodologies and discuss challenges and open questions. In the appendix, we provide tables that summarize topics and methods. We also provide an interactive online platform to navigate each reference: https://boschresearch.github.io/multimodalperception/. 0.99 0.8 0.98 0.99 0.96 0.96 0.94 Vehicle Person Road sign Traffic light LiDAR Points Map Radar Points RGB Image

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“…The settings being used in this paper are summarized in Table IV. For the Bosch Small Traffic Lights [69] and the KITTI datasets, the dimensions of C 6 are higher than that of the MIO-TCD dataset, so we reduce the number of convolutional kernels by half in conv3 (i.e., 64 → 32), in order to reduce the number of parameters in the fc1 layer of the transformation sub-network. We note that, however, that our model is somewhat robust to such changes in the network architecture.…”

Section: F Training Detailsmentioning

confidence: 99%

Learning a Layout Transfer Network for Context Aware Object Detection

Wang

Cai

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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We present a context aware object detection method based on a retrieve-and-transform scene layout model. Given an input image, our approach first retrieves a coarse scene layout from a codebook of typical layout templates. In order to handle large layout variations, we use a variant of the spatial transformer network [1] to transform and refine the retrieved layout, resulting in a set of interpretable and semantically meaningful feature maps of object locations and scales. The above steps are implemented as a Layout Transfer Network which we integrate into Faster RCNN [2] to allow for joint reasoning of object detection and scene layout estimation. Extensive experiments on three public datasets verified that our approach provides consistent performance improvements to the state-ofthe-art object detection baselines on a variety of challenging tasks in the traffic surveillance and the autonomous driving domains.

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A deep learning approach to traffic lights: Detection, tracking, and classification

Cited by 195 publications

References 16 publications

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

Learning a Layout Transfer Network for Context Aware Object Detection

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