# Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation Source: [Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:58:11.540Z Authors: Liangfu Chen, Zeng Yang, Jianjun Ma, Zheng Luo Published: 2018-03-10T08:55:46.000Z ## Abstract As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture. The proposed network model, which we named Driving Scene Perception Network (DSPNet), uses multi-level feature maps and multi-task learning to improve the accuracy and efficiency of object detection, depth estimation and image segmentation tasks from a single input image. Hence, the resulting network model uses less than 850 MiB of GPU memory and achieves 14.0 fps on NVIDIA GeForce GTX 1080 with a 1024x512 input image, and both precision and efficiency have been improved over combination of single tasks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()