# LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation Source: [LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:11:27.283Z Authors: Taha Emara, Hossam E. Abd El Munim, Hazem M. Abbas Published: 2019-12-13T20:05:52.000Z ## Abstract Semantic image segmentation plays a pivotal role in many vision applications including autonomous driving and medical image analysis. Most of the former approaches move towards enhancing the performance in terms of accuracy with a little awareness of computational efficiency. In this paper, we introduce LiteSeg, a lightweight architecture for semantic image segmentation. In this work, we explore a new deeper version of Atrous Spatial Pyramid Pooling module (ASPP) and apply short and long residual connections, and depthwise separable convolution, resulting in a faster and efficient model. LiteSeg architecture is introduced and tested with multiple backbone networks as Darknet19, MobileNet, and ShuffleNet to provide multiple trade-offs between accuracy and computational cost. The proposed model LiteSeg, with MobileNetV2 as a backbone network, achieves an accuracy of 67.81% mean intersection over union at 161 frames per second with $640 \\times 360$ resolution on the Cityscapes dataset. ## 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. - [Semantic Segmentation on Cityscapes (test)](): Semantic segmentation; Cityscapes (test); mIoU: not recorded. ## Original sources and implementation [Original paper]() [PDF]()