Abstract
For self-driving vehicles, detecting lane lines in changeable scenarios is a fundamental yet challenging task. The rise of deep learning in recent years has contributed to the thriving of autonomous driving. However, existing methods of lane detection based on deep learning have high requirements on computing environment, so their applicability is further restricted. This paper proposed an improved attention deep neural network (DNN), a lightweight semantic segmentation architecture catering for efficient computation in low memory, which contains two branches worked in different resolution. The proposed network integrates fine details captured by local interaction of pixels at high resolution into global contexts at low resolution, computing dense feature maps for prediction task. Based on the attributes of disparate feature resolution characteristics, different attention mechanisms are adopted to guide the network to effectively exploit the model parameters. The proposed network achieves comparable results with state-of-the-art methods on two popular lane detection benchmarks (TuSimple and CULane), with faster calculation efficiency at 259 frames-per-second (FPS) on CULane dataset, and the total number of model parameters only requires 1.57 M. This study provides a practical and meaningful reference for the application of lane detection in memory constrained devices.
Highlights
Lane detection is a research hotspot in autonomous driving, and it is a key technology of Advanced Driver Assistance System (ADAS) [1]. e forward-looking camera mounted behind the front windshield is used to collect the surrounding driving environment. e vision-based processing algorithm is embedded in the vehicle to detect lane lines from captured video clips. en the results of lane detection will be applied to subsequent tasks including lane keeping [2], trajectory planning, and behavior prediction. erefore, lane detection is an integral part of automatic driving and ADAS
Lane detection scenarios are subject to changes in illumination, weather conditions, traffic congestion, and other factors, which varies during driving and poses great challenges to lane estimation. ese challenges are inevitable but can hamper the detection accuracy, especially when using traditional methods. us, we need a more robust method to meet high precision requirements of lane detection in challenging environments
Attention mechanism originated from the research of natural language processing (NLP) and was gradually applied to the field of CV recently. is mechanism can selectively focus on important features and suppress irrelevant features, improving the performance of deep neural network (DNN)
Summary
Lane detection is a research hotspot in autonomous driving, and it is a key technology of Advanced Driver Assistance System (ADAS) [1]. e forward-looking camera mounted behind the front windshield is used to collect the surrounding driving environment. e vision-based processing algorithm is embedded in the vehicle to detect lane lines from captured video clips. en the results of lane detection will be applied to subsequent tasks including lane keeping [2], trajectory planning, and behavior prediction. erefore, lane detection is an integral part of automatic driving and ADAS. Recent studies on lane detection have shown that methods based on deep learning can deliver strikingly better results than traditional methods that depend on hand-crafted cues. Compared with traditional algorithms that highly rely on vision cues in specific environments, deep learning-based methods improve the network’s scene perception ability by continuously optimizing neural network parameters, which attain higher robustness and applicability. Lane detection methods based on semantic segmentation and instance segmentation [6–9] have attracted extensive attention in recent years Some of these recent approaches have tried to directly adopt a classic segmentation network or its variants to segment lane markings. One branch named global context embedding (GCE) focuses on capturing global information that can be used to deduce heavily occluded and blurred lane markings Another branch explicit boundary regression (EBR) tries to exploit spatial attention mechanism (SAM) to aggregate boundary information at different locations, and a supervisory operation by label’s edge information is attached at the end of EBR. Related Works e present work relies heavily on prior efforts in lane detection and attention mechanism areas
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