Semi-Supervised Lane Detection With Deep Hough Transform

Lin, Yancong; Pintea, Silvia L.; Gemert, Jan C. van

doi:10.1109/icip42928.2021.9506299

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…In [24], the authors used lane-mark colors to eliminate the influence of moving vehicles and lighting conditions. In addition, several methods applied the Hough transform [25], particle filtering [26] and Gabor filtering [27] to lane detection tasks. Song et al [28] used a maximum likelihood angle to design a self-adaptive traffic lanes model in Hough Space.…”

Section: A Lane Detection Methodsmentioning

confidence: 99%

MLP-Based Efficient Convolutional Neural Network for Lane Detection

Yao

Wang

et al. 2023

IEEE Trans. Veh. Technol.

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Lane detection is an important and fundamental task in autonomous driving. Modern convolutional neural network (CNN) methods have achieved high performance in lane detection; however, the intrinsic locality of convolution operations makes these methods limited in effectively modeling the long-range dependencies that are vital to capture global information of lanes. Additionally, numerous convolution operations result in considerable computational cost for high complexity. To overcome these difficulties, we propose an efficient lane detection method by combining CNN with a multilayer perceptron (MLP). First, an improved bottleneck-1D layer is used to replace the standard convolutional layer in overall network to reduce the computational cost and parameters while applying hybrid dilated convolution (HDC) to better capture multiscale lane information. Second, we construct a hybrid MLP block in the latent space to capture the long-range dependencies of lanes. The hybrid MLP projects tokenized convolutional features from spatial locations and channels, and then, they are fused together to obtain global representation, in which each output pixel is related to each input pixel. The introduction of MLP further decreases computational complexity and makes the proposed architecture more efficient for lane detection. Experimental results on two challenging datasets (CULane, Tusimple) demonstrate that our method can achieve a higher computational efficiency while maintaining a decent detection performance compared with other state-of-the-art methods. Furthermore, this study indicates that integrating the global representation capacity of an MLP with local prior information of convolution is an effective and potential perspective in lane detection.

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Section: A Lane Detection Methodsmentioning

confidence: 99%

MLP-Based Efficient Convolutional Neural Network for Lane Detection

Yao

Wang

et al. 2023

IEEE Trans. Veh. Technol.

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“…In our approach, the integration of the Differentiable Hough Transform (DHT) into the AttHT-IHT at a specific deep feature scale is a strategic decision driven by both efficiency considerations and alignment with previously successful applications [37], [38]. The application of AttHT-IHT at this level is designed to amplify and emphasize the most critical lines essential for our primary objective of building segmentation.…”

Section: B Placement Of Attht-ihtmentioning

confidence: 99%

An Attentive Hough Transform Module for Building Extraction From High Resolution Aerial Imagery

Yahia Berrouiguet,

Zigh,

Djebouri

2024

IEEE Access

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In the era of abundant high-resolution aerial imagery, the automatic extraction of buildings is indispensable for applications like disaster response, environmental monitoring, and urban growth analysis. Deep learning approaches, particularly fully convolutional networks, have exhibited remarkable performance in this challenging task. Nevertheless, the accurate identification and delineation of building boundaries pose persistent challenges hindering further improvements in building extraction precision. To tackle these, we introduce a novel deep learning architecture explicitly designed for building extraction in high-resolution aerial images. Our method addresses the precise identification of building borders by combining both local and global contextual information. We efficiently preserve object boundaries and optimize the representation of straight lines within buildings through the integration of the Attentive Hough Transform and Inverse Hough Transform (AttHT-IHT) module into the U-Net architecture. Extensive experiments on the Potsdam dataset showcase substantial enhancements in building extraction accuracy, with a 97.73 % accuracy rating and a 96.42 % recall rate. Generalization capability on the WHU satellite dataset I was assessed to validate the adaptability of our proposed method.

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“…However, it has poor detection accuracy. HTpseudo+LHT (Lin et al., 2021) is proposed by adopting Hough transform loss. By learning from a large number of unlabeled images, the proposed method significantly improves the performance.…”

Section: Related Workmentioning

confidence: 99%

A real‐time lane detection network using two‐directional separation attention

Zhang

Jiang

Yang

et al. 2023

Computer aided Civil Eng

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Real‐time network by adopting attention mechanism is helpful for enhancing lane detection capability of autonomous vehicles. This paper proposes a real‐time lane detection network (TSA‐LNet) that incorporates a lightweight network (LNet) and a two‐directional separation attention (TSA) to enhance the lane detection capability of autonomous vehicles. By adopting the attention mechanism, the real‐time performance and detection accuracy are significantly improved. Specifically, LNet employs symmetry layer to drastically reduce the number of parameters and the network's running time. TSA infers the attention map along two separate directions, transverse and longitudinal, and performs adaptive feature refinement by multiplying the attention map with the input feature map. TSA can be integrated into LNet to capture the local textural and global contextual information of lanes without increasing the processing time. Results on popular benchmarks demonstrate that TSA‐LNet achieves outstanding detection accuracy and faster speed (6.99 ms per image). Additionally, TSA‐LNet exhibits excellent robustness in real‐world scenarios.

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Semi-Supervised Lane Detection With Deep Hough Transform

Cited by 9 publications

References 27 publications

MLP-Based Efficient Convolutional Neural Network for Lane Detection

MLP-Based Efficient Convolutional Neural Network for Lane Detection

An Attentive Hough Transform Module for Building Extraction From High Resolution Aerial Imagery

A real‐time lane detection network using two‐directional separation attention

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