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M. Sakamoto

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Open access Jul 2026

A Lightweight CNN–Mamba Hybrid Architecture for Efficient Crack Segmentation

Abstract. Pavement crack segmentation is important for road infrastructure inspection, but practical deployment remains challenging because many high-performance deep learning models require substantial computational resources. This issue is particularly critical in large-scale Mobile Mapping System (MMS)-based workflows, where large volumes of road surface images must be processed efficiently. To address this problem, this study proposes a lightweight CNN-Mamba hybrid architecture for crack segmentation as a deployment-oriented redesign of CT-CrackSeg. The proposed method replaces the original MobileViT-based global modelling modules with EfficientViM-inspired blocks based on hidden-state mixer-based state space duality (HSM-SSD), while preserving the overall encoder-decoder structure and refining the boundary enhancement branch with DCNv2-based deformable convolution. Experiments on the publicly available GAPs384 and CamCrack789 datasets show that the proposed model maintains competitive topology-aware segmentation performance while substantially improving computational efficiency. Compared with CT-CrackSeg, the proposed model increases inference speed from 1.49 to 4.44 FPS on GAPs384 and from 1.32 to 3.92 FPS on CamCrack789, while reducing peak memory consumption from 2827 MB to 355 MB on both datasets. At the same time, the clDice score remains comparable, changing from 0.760 to 0.758 on GAPs384 and from 0.921 to 0.922 on CamCrack789. These results indicate that the proposed architecture provides a favourable balance between crack segmentation quality and deployment efficiency, making it a practical option for large-scale pavement inspection and photogrammetric infrastructure monitoring.

Masaya Shimasaki, M. Sakamoto, Toshiaki Satoh · 0 citations
Open access Jul 2026

A Multi-Stage Deep Learning Framework for Shadow Detection in Aerial Orthophotos

Abstract. Shadow correction is an important preprocessing step not only for visual enhancement but also for improving object recognition performance in remote sensing imagery. Although many datasets and deep learning models have been proposed for shadow detection and removal, most of them focus on natural images. In contrast, high-resolution aerial orthophotos contain large continuous shadows caused by tall buildings, especially in urban areas, and existing models often fail to handle such large-scale structures effectively.In this study, we construct a new shadow annotation dataset specifically designed for aerial orthophotos with spatial resolutions of 20 cm/pixel and 5 cm/pixel. Furthermore, we propose a three-stage multi-resolution segmentation framework that progressively refines shadow predictions from low to high resolution. Predictions from lower-resolution stages are used as auxiliary information to guide higher-resolution prediction. Experimental results demonstrate that the proposed approach improves fuzzy Intersection over Union (IoU) by approximately 0.05 compared with a previously published shadow detection model, and also outperforms a single-stage baseline, particularly for large continuous shadow regions. The framework is also applicable to other large-scale segmentation tasks requiring extensive receptive fields.

Yohei Kobayashi, M. Sakamoto, Sho Nakamura et al. · 0 citations