Combination of modified U‐Net and domain adaptation for road detection

Dong, Ming; Zhao, Xiangmo; Fan, Xing; Shen, Chao; Liu, Zhanwen

doi:10.1049/iet-ipr.2018.6696

Cited by 6 publications

(5 citation statements)

References 45 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, for each pixel of the image, which was a path or a lane, authors Dong et al considered the visual road-detection challenge applying a U-Net-prior network with the DAM (Domain Adaptation Model) to reduce the disparity between the training images and the test image [80]. The proposed model was compared to other state-of-art methods such as RBNet [191], StixeNet II and MultiNet [192], where the max-F measures were 94.97%, 94.88% and 94.88%, respectively, in 0.18 s, 1.2 s and 1.7 s. Their methodology obtained 95.57% max F-measurement in 0.15 s faster and more accurately than others, which indicates that their monocular-vision-based systems achieve high precision for a lower running time.…”

Section: Lane Detection and Trackingmentioning

confidence: 99%

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

2022

View full text Add to dashboard Cite

In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. However, the AVS is still far away from mass production because of the high cost of sensor fusion and a lack of combination of top-tier solutions to tackle uncertainty on roads. To reduce sensor dependency and to increase manufacturing along with enhancing research, deep learning-based approaches could be the best alternative for developing practical AVS. With this vision, in this systematic review paper, we broadly discussed the literature of deep learning for AVS from the past decade for real-life implementation in core fields. The systematic review on AVS implementing deep learning is categorized into several modules that cover activities including perception analysis (vehicle detection, traffic signs and light identification, pedestrian detection, lane and curve detection, road object localization, traffic scene analysis), decision making, end-to-end controlling and prediction, path and motion planning and augmented reality-based HUD, analyzing research works from 2011 to 2021 that focus on RGB camera vision. The literature is also analyzed for final representative outcomes as visualization in augmented reality-based head-up display (AR-HUD) with categories such as early warning, road markings for improved navigation and enhanced safety with overlapping on vehicles and pedestrians in extreme visual conditions to reduce collisions. The contribution of the literature review includes detailed analysis of current state-of-the-art deep learning methods that only rely on RGB camera vision rather than complex sensor fusion. It is expected to offer a pathway for the rapid development of cost-efficient and more secure practical autonomous vehicle systems.

show abstract

Section: Lane Detection and Trackingmentioning

confidence: 99%

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

2022

View full text Add to dashboard Cite

show abstract

“…The total loss is obtained by the weighted summation of the above branch losses, as shown in formula (5):…”

Section: Loss Functionsmentioning

confidence: 99%

“…With the progress and development of computer vision technology [1,2], as one of its basic tasks, target detection [3] is becoming more and more important in the fields of safety monitoring [4] and autopilot [5]. However, visible-light images require stable imaging conditions to ensure good performance and are greatly affected by light.…”

Section: Introductionmentioning

confidence: 99%

I‐CenterNet: Road infrared target detection based on improved CenterNet

Qian

Guo³

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

Infrared target detection has strong anti-interference ability, long working distance and can work day and night. So it is widely used in military security and transportation fields, and infrared road object detection is critical in traffic checkpoints and autonomous driving. However, the target scale in infrared images changes greatly, small targets are difficult to detect, the poor image quality and low signal-to-noise ratio are still huge challenges in infrared target detection. This paper proposes an improved infrared target detection model I-CenterNet based on the anchor-free model CenterNet. The EfficientNetV2 with the channel attention mechanism is used instead of the traditional structure as the backbone network to enhance feature extraction. In order to reduce the noise of the input infrared image, Dilated-Residual U-net (DRUNet) is used. Meanwhile, feature pyramid and Sub-Pixel are combined for multi-scale feature fusion. Data enhancement is implemented to improve model performance. The experimental results show that the average detection accuracy of this model on the Flir infrared data set is 87.9%, and the average detection speed reaches 14.2 frames/s.

show abstract

“…Currently, we are focused on treating the monocular camera images [17,18]. In this field, one basic algorithm framework of road detection can be divided into feature extraction and classification.…”

Section: Road Detectionmentioning

confidence: 99%

A two‐scaled fully convolutional learning network for road detection

Liang

2021

IET Image Processing

View full text Add to dashboard Cite

This paper aims to detect road regions based on a two‐scaled deep neural network. The information from different scales is helpful to boost the performance of deep learning models, and it is also a widely used strategy in various computer vision applications. In the two‐scaled model, skip‐architecture and fully convolutional layers are used to fuse the low‐level details and high‐level semantic information. It enables to detect the road areas by multi‐scale feature maps from different reception fields. To avoid the redundancy of scale information and the loss of features caused by the pooling layer, the feature maps before the first pooling layer are adopted in our model. By the convolutional kernels, our model can balance the information of two scales automatically. The loss function is also improved, in which the intersection over union (IoU) term is taken into account to guide the model to learn more features on the whole road regions. Comprehensive experiments on three benchmark datasets demonstrate that this approach can reach state‐of‐the‐art performance.

show abstract

Combination of modified U‐Net and domain adaptation for road detection

Cited by 6 publications

References 45 publications

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

I‐CenterNet: Road infrared target detection based on improved CenterNet

A two‐scaled fully convolutional learning network for road detection

Contact Info

Product

Resources

About