Sawant, Shrutika ShankarShrutika ShankarSawantMedgyesy, AndreasAndreasMedgyesyRaghunandan, SahanaSahanaRaghunandanGötz, TheresaTheresaGötz2025-04-282025-04-282025https://publica.fraunhofer.de/handle/publica/48702710.5220/00133438000038902-s2.0-105001708320U-Net, an encoder-decoder architecture is the most popular choice in the semantic segmentation field due to its ability to learn rich semantic features while handling enormous amounts of data. However, due to large number of parameters and slow inference, deploying U-Net on devices with limited computational resources such as mobile and embedded devices becomes challenging. To alleviate the above challenge, in this study, we propose an efficient, lightweight, and robust encoder-decoder architecture, LMSC-UNet for semantic segmentation that captures more comprehensive, contextual information and effectively learns rich semantic features. This lightweight architecture considerably reduces the number of trainable parameters, requiring sufficiently less memory space, training, and inference time. Skip connections in original U-Net fuse features from each encoder block to the corresponding decoder block. This simple skip connection reduces the semantic gap to some extent and may limit the segmentation performance. Therefore, we replace the skip connection from the second level of U-Net with a bottleneck residual block (BRB) which helps to enhance the final segmentation map by lessening the semantic gap between the features of decoder with the corresponding features of encoder. Extensive experiments on various segmentation datasets from diverse domains demonstrate the effectiveness of our proposed approach. The experimental results show that the compact model speeds up the inference process, while still maintaining the performance. When compared to the standard U-Net, LMSC-UNet has achieved 7× reduction in Floating Point Operations (FLOPs), and 34× reduction in model size, while maintaining the segmentation accuracy.enfalseDepthwise Separable ConvolutionsSemantic SegmentationSkip ConnectionsU-Netconference paper