Instance segmentation of traffic participants using vision-based techniques serves as a cornerstone for numerous intelligent transportation systems. Although existing deep learning-based methods have made significant advancements in this field, these algorithms still present considerable challenges with regard to generalizability for commercial deployment. Specifically, the mean average perception (mAP) of these algorithms degrades rapidly when dealing with intersections that are not included in the training set. To address this limitation, a novel instance segmentation approach named Si-GAIS is proposed, which incorporates a siamese structure for the first time with the proposed Generalizable-Attention Encoder (GA). Through the proposed Foreground-Background Fusion Unit (FBF) within GA, efficient feature-level fusion for the foreground and background images is achieved. Additionally, the Interpretable Attention Neck (IA) in GA enables the feature encoder to focus exclusively on the foreground traffic participants while ignoring various backgrounds. To utilize Si-GAIS, an unsupervised method named P-DBSCAN is proposed to obtain high-quality background image for each intersection with slow-moving traffic and camera jitters. Finally, the first multi-intersection multi-category instance segmentation datasets named RopeIns is proposed for validation. Si-GAIS achieves a 7.7% mAP (All APs used in this paper are abbreviations of AP $_{\textit {50}}$ the same with PASCAL VOC.) accuracy improvement compared to the state-of-the-art (SOTA) methods while using fewer parameters, with only a 6.4% decline in AP for car segmentation in unseen intersections and weather conditions, whereas all other SOTA methods decline more than 10%. The proposed dataset and source code are publicly available at https://github.com/441599828/SiGAIS and we hope Si-GAIS will be a new baseline for IPS instance segmentation research.


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    Title :

    Si-GAIS: Siamese Generalizable-Attention Instance Segmentation for Intersection Perception System


    Contributors:
    Wang, Huanan (author) / Zhang, Xinyu (author) / Wang, Hong (author) / Jun, Li (author)

    Published in:

    Publication date :

    2024-11-01


    Size :

    7051288 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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