Vehicle detection and distance estimation are critical components of driver assistance system and self‐driving system, and considerable different frameworks have been investigated such as radar, laser and camera‐based. Among them, camera‐based vehicle detection and distance estimation have an obvious advantage over other systems in that it needs lower cost. However, existing camera‐based methods are not robust enough under complex driving scenes. In this work, an end‐to‐end deep convolutional neural network framework is proposed to jointly detect vehicles and estimate vehicle distance efficiently. Specifically, a monocular depth estimation method is designed to transform the RGB appearance information into depth modality information. Then the vehicle detection module takes the RGB and depth image as inputs to improve the detection performance. Finally, the distance estimation module employs the detection results and the estimated depth information to predict the distance more precisely. The whole network can be trained in an end‐to‐end manner with the multi‐task loss function. The proposed framework is evaluated on the public vehicle detection benchmark KITTI to show the effectiveness of the proposed framework. Moreover, the performance of three proposed sub‐modules are also analysed separately to give a more comprehensive evaluation of the designed framework.
Joint vehicle detection and distance prediction via monocular depth estimation
IET Intelligent Transport Systems ; 14 , 7 ; 753-763
2020-07-01
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
cameras , vehicle distance , driver information systems , image sensors , camera‐based vehicle detection , self‐driving system , object detection , joint vehicle detection , depth modality information , traffic engineering computing , public vehicle detection benchmark KITTI , convolutional neural nets , estimated depth information , road vehicles , image colour analysis , end‐to‐end deep convolutional neural network framework , driver assistance system , distance estimation module , complex driving scenes , computer vision , RGB appearance information , vehicle detection module , monocular depth estimation method , depth image , detection performance , critical components
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