Detection of the drivable area on high-speed road via YOLACT.

In intelligent driving, the detection and ranging of drivable area are key technologies in path planning. In order to realize quick and accurate detection and segmentation of drivable area, we adopt YOLACT_ResNet38_TFPN network as an improved design of YOLACT. The original YOLACT has ResNet101 residual structure. We reduce the network layers to ResNet38 and add C6 and C7 with larger receiving field to improve the detection speed in the drivable area. Moreover, the FPN (feature pyramid network) is designed as a structure of three sizes to match C6 and C7. According to the characteristics of road image, the aspect ratio of three anchor points is redesigned to further improve the detection accuracy and speed. A univariate linear regression model is designed to accurately calculate the distance of the drivable area. The model parameters are achieved by multivariate linear fitting method based on multiple sets of distance measurement data. Finally, YOLACT_ResNet38_TFPN's FPS for the drivable area is 46.13, the box mAP is 62.36, and the mask mAP is 61.36. The maximum ranging error of this method within 96.90 m is 5.3 m. It can quickly and accurately measure the drivable distance and provide reasonable path driving suggestions for intelligent driving. [ABSTRACT FROM AUTHOR]