Lane detection based on machine vision,a key application in intelligent transportation,is generally characterized by gradient information of lane edge and plays an important role in advanced driver assistance systems(...Lane detection based on machine vision,a key application in intelligent transportation,is generally characterized by gradient information of lane edge and plays an important role in advanced driver assistance systems(ADAS).However,gradient information varies with illumination changes.In the complex scenes of urban roads,highlight and shadow have effects on the detection,and non-lane objects also lead to false positives.In order to improve the accuracy of detection and meet the robustness requirement,this paper proposes a method of using top-hat transformation to enhance the contrast and filter out the interference of non-lane objects.And then the threshold segmentation algorithm based on local statistical information and Hough transform algorithm with polar angle and distance constraint are used for lane fitting.Finally,Kalman filter is used to correct lane lines which are wrong detected or missed.The experimental results show that computation times meet the real-time requirements,and the overall detection rate of the proposed method is 95.63%.展开更多
Instance segmentation has drawn mounting attention due to its significant utility.However,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level...Instance segmentation has drawn mounting attention due to its significant utility.However,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level labeling.In this paper,we present a conceptually efficient contour regression network based on the you only look once(YOLO)architecture named YOLO-CORE for instance segmentation.The mask of the instance is efficiently acquired by explicit and direct contour regression using our designed multiorder constraint consisting of a polar distance loss and a sector loss.Our proposed YOLO-CORE yields impressive segmentation performance in terms of both accuracy and speed.It achieves 57.9%AP@0.5 with 47 FPS(frames per second)on the semantic boundaries dataset(SBD)and 51.1%AP@0.5 with 46 FPS on the COCO dataset.The superior performance achieved by our method with explicit contour regression suggests a new technique line in the YOLO-based image understanding field.Moreover,our instance segmentation design can be flexibly integrated into existing deep detectors with negligible computation cost(65.86 BFLOPs(billion float operations per second)to 66.15 BFLOPs with the YOLOv3 detector).展开更多
文摘Lane detection based on machine vision,a key application in intelligent transportation,is generally characterized by gradient information of lane edge and plays an important role in advanced driver assistance systems(ADAS).However,gradient information varies with illumination changes.In the complex scenes of urban roads,highlight and shadow have effects on the detection,and non-lane objects also lead to false positives.In order to improve the accuracy of detection and meet the robustness requirement,this paper proposes a method of using top-hat transformation to enhance the contrast and filter out the interference of non-lane objects.And then the threshold segmentation algorithm based on local statistical information and Hough transform algorithm with polar angle and distance constraint are used for lane fitting.Finally,Kalman filter is used to correct lane lines which are wrong detected or missed.The experimental results show that computation times meet the real-time requirements,and the overall detection rate of the proposed method is 95.63%.
基金supported by the National Key R&D Program of China(Nos.2018AAA0100104 and 2018AAA0100100)Natural Science Foundation of Jiangsu Province,China(No.BK20211164).
文摘Instance segmentation has drawn mounting attention due to its significant utility.However,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level labeling.In this paper,we present a conceptually efficient contour regression network based on the you only look once(YOLO)architecture named YOLO-CORE for instance segmentation.The mask of the instance is efficiently acquired by explicit and direct contour regression using our designed multiorder constraint consisting of a polar distance loss and a sector loss.Our proposed YOLO-CORE yields impressive segmentation performance in terms of both accuracy and speed.It achieves 57.9%AP@0.5 with 47 FPS(frames per second)on the semantic boundaries dataset(SBD)and 51.1%AP@0.5 with 46 FPS on the COCO dataset.The superior performance achieved by our method with explicit contour regression suggests a new technique line in the YOLO-based image understanding field.Moreover,our instance segmentation design can be flexibly integrated into existing deep detectors with negligible computation cost(65.86 BFLOPs(billion float operations per second)to 66.15 BFLOPs with the YOLOv3 detector).
