Fast-SegNet: fast semantic segmentation network for small objects

doi:10.1007/s11042-024-18829-1

2024-03-09

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DOI：10.1007/s11042-024-18829-1

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摘要

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Semantic segmentation is a fundamental step in image understanding, playing a crucial role in the fields of automatic driving, medical image analysis, defect detection, etc. Despite significant progress in deep learning-based image segmentation, challenges in terms of accuracy and efficiency still exist, especially for small-scale objects. In this paper, we present a novel data augmentation method for small-scale objects in images, aiming to address the issue of class imbalance. Specifically, we extract small-scale objects from one image and then copy-scale-and-paste them to other images. Additionally, a novel multi-scale feature fusion module is proposed to effectively combine features from both deep and shallow neural network layers. Subsequently, the data augmentation method and multi-scale feature fusion module are utilized in the proposed Fast-SegNet architecture for semantic segmentation. Extensive experiments demonstrate that Fast-SegNet could improve segmentation performance, especially for small-scale objects with an acceptable computational cost. State-of-the-art performance has been achieved on CamVid, CityScapes, and MOST (Micro-optical sectioning tomography) datasets with respect to the tradeoff between accuracy and speed. Specifically, the CamVid dataset yields mean IoU (Intersection over Union) values of 45.7% and 38.6% for small-scale objects as Pedestrian and Bicyclist, respectively. The CityScapes dataset demonstrates mean IoU of 43.43% and 43.56% for small-scale objects as Traffic Light and Rider, respectively. The MOST dataset results in a segmentation mean IoU of 88.2% for vessels in the mouse brain. In conclusion, our approach achieves better results in terms of accuracy and efficiency on three datasets. Codes are available at https://github.com/apple1986/Fast-SegNet.

Keyword:

Deep learning

Small-scale

Fast segmentation

Data augmentation

Feature fusion