Fully Convolutional Network Variations and Method on Small Dataset

Hu, Tianyou; Deng, Yancong; Deng, Yuwei; Ge, Anmin

doi:10.1109/iccece51280.2021.9342059

Cited by 8 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, we observe that the proposed network is better suitable for practical uses and real-time applications than its competitors [14], [37]. Concerning the mIoU metric, we notice that our proposed RBANet outperforms the majority in the state of the art [17], [19], also there is a wide difference with some models [10], [12], [38]. In terms of the parameters' number, our RBANet outperformed the bulk of similar research as seen in Table 3.…”

Section: Resultsmentioning

confidence: 56%

Residual balanced attention network for real-time traffic scene semantic segmentation

Kherraki

Warraich²,

Maqbool³

et al. 2023

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic scene semantic segmentation using CNNs requires high precision with few computational resources to perceive and segment the scene in real-time. However, we often find related work focusing only on one aspect, the precision, or the number of computational parameters. In this regard, we propose RBANet, a robust and lightweight CNN which uses a new proposed balanced attention module, and a new proposed residual module. Afterward, we have simulated our proposed RBANet using three loss functions to get the best combination using only 0.74M parameters. The RBANet has been evaluated on CamVid, the most used dataset in semantic segmentation, and it has performed well in terms of parameters’ requirements and precision compared to related work.</span>

show abstract