With the rapiddevelopment of deep learning in recent years, the level of automatic driving perception has also increased substantially. However, automatic driving perception under adverse conditions, such as fog, rem...
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With the rapiddevelopment of deep learning in recent years, the level of automatic driving perception has also increased substantially. However, automatic driving perception under adverse conditions, such as fog, remains a significant obstacle. The existing fog-orienteddetection algorithms are unable to simultaneously address the detection accuracy anddetection speed. Based on improved YOLOv5, this work provides a multiobjectdetection network for fog driving scenes. We construct a synthetic fog dataset by using the dataset of a virtual scene and the depth information of the image. Second, we present a detection network for driving in fog based on improved YOLOv5. The ResNeXt model, which has been modified by structural re-parameterization, serves as the model's backbone. We build a new feature enhancement module (FEM) in response to the lack of features in fog scene images and use the attention mechanism to help the detection network pay more attention to the more useful features in the fog scenes. The test results show that the proposed fog multitarget detection network outperforms the original YOLOv5 in terms of detection accuracy and speed. The accuracy of the Real-world Task-driven Testing Set (RTTS) public dataset is 77.8%, and the detection speed is 31 frames/s, which is 14 frames faster as compared with the original YOLOv5.
With the rapiddevelopment of society and the economy, autonomous driving techniques are widely applied in many areas, such as autonomous vehicles, autonomous drones, and robotics. As a dominating technique, deep lear...
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With the rapiddevelopment of society and the economy, autonomous driving techniques are widely applied in many areas, such as autonomous vehicles, autonomous drones, and robotics. As a dominating technique, deep learning has become more and more popular for 2-d and 3-dobjectdetection. Numerous deep learning-based methods have been proposed to solve various vision issues. To further help with the development of unmanned systems, this paper presents a comprehensive survey of the recent processes from the past five years for 3-dobjectdetection, roaddetection, traffic sign detection, and traffic light detection and classification. To summarize and analyze previous works in detail, this paper only focuses on deep learning-basedobjectdetection tasks in autonomous driving that take place when the input is a point cloud or image(s). It also presents comparative results for insight comparison and inspiring future researches. (c) 2022 Elsevier B.V. All rights reserved.
As self-driving vehicles become more prevalent, the speed and accuracy of detecting surrounding objects through onboard sensing technology have become increasingly important. The YOLOv8-QSd network is a novel anchor-f...
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As self-driving vehicles become more prevalent, the speed and accuracy of detecting surrounding objects through onboard sensing technology have become increasingly important. The YOLOv8-QSd network is a novel anchor-free driving scene detection network that builds on YOLOv8 and ensures detection accuracy while maintaining efficiency. The network's backbone employs structural reparameterization techniques to transform the diverse branch block (dBB)-based model. To accurately detect small objects, it integrates features of different scales and implements a bidirectional feature pyramid network (BiFPN)-based feature pyramid after the backbone. To address the challenge of long-range detection in driving scenarios, a query-based model with a new pipeline structure is introduced. The test results demonstrate that this algorithm outperforms YOLOv8 on the large-scale small objectdetectiondataset (SOdA-A) in terms of both speed and accuracy. With an accuracy rate of 64.5% and reduced computational requirements of 7.1 GFLOPs, it satisfies the speed, precision, and cost-effectiveness requirements for commercial vehicles in high-speed roaddriving scenarios.
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