{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T10:58:30Z","timestamp":1771844310445,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>The detection of micro-inclusions and the representation of interpretable results compatible with gemological standards are two major bottlenecks to the automation of diamond clarity grading. In this work, we propose a novel solution using an enhanced YOLOv7 model in a multimodal framework to overcome the above bottlenecks. Specifically, our contributions are threefold: Firstly, we improve the original YOLOv7 by incorporating the Efficient Channel Attention (ECA) mechanism to enhance the extracted fine-grained features and the Adaptively Spatial Feature Fusion (ASFF) module for capturing more robust multi-scale representations; secondly, we build a three-channel input consisting of optical grayscale, gray-level co-occurrence matrix (GLCM) texture linked with the optical properties of inclusions, and morphological operation-enhanced images. Thirdly, we design a traceable grading system by combining the XGBoost classifier with programmable GIA (Gemological Institute of America) rules. Our method achieves 91.3% mAP@0.5 on the Roboflow Diamond Inclusion dataset, outperforming the baseline YOLOv7 by 9.2%. The clarity grading performance on this dataset attains an accuracy of 86.7%, a Kappa coefficient of 0.82, and a weighted F1-score of 0.87, resulting in high consistency with human expert evaluations. Ablation experiments confirm that the proposed components all make individual and complementary contributions. This work represents a significant advance towards the automatic, accurate, and interpretable grading of diamonds, and it creates a practical tool for use within the jewelry industry.<\/jats:p>","DOI":"10.31449\/inf.v50i8.12773","type":"journal-article","created":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T11:55:52Z","timestamp":1771761352000},"source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Diamond Inclusion Detection and Clarity Grading via Enhanced YOLOv7 with ECA and ASFF Modules"],"prefix":"10.31449","volume":"50","author":[{"given":"Xiaobing","family":"HUO","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,21]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12773\/6521","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12773\/6521","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T10:01:19Z","timestamp":1771840879000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12773"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":0,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,2,21]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i8.12773","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,21]]}}}