{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T23:47:18Z","timestamp":1768607238233,"version":"3.49.0"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"37","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Object recognition across varying scales remains a persistent challenge in computer vision, especially in scenes with occlusion, low contrast, and diverse spatial resolutions. Conventional convolutional neural networks with fixed receptive fields often fail to capture both fine-grained details and high-level contextual cues. This study focuses on developing a scale-adaptive detection framework to overcome these limitations. The proposed MSFNet (Multiscale Fusion Network) employs a Dual-Stream Convolutional Backbone to extract low-level and high-level features in parallel. A Scale-Adaptive Feature Fusion Module (SAFFM) integrates multiscale representations through dynamic, scale-aware weighting. A Cross-Scale Attention Refinement (CSAR) module enhances discriminative features and suppresses irrelevant or redundant information. The architecture operates in an end-to-end fashion and is optimized for detection accuracy and real-time inference speed. Experimental evaluation on MS COCO 2017 and PASCAL VOC 2012 reports 47.3% AP and 81.5% mAP, respectively. Performance exceeds Faster R-CNN, YOLOv5, and RetinaNet by +3.8%, +4.5%, and +3.2% AP on the COCO benchmark. MSFNet provides a scalable, accurate, and computationally efficient approach for multiscale object recognition, enabling deployment in real-time applications such as autonomous driving, intelligent surveillance, and remote sensing.<\/jats:p>","DOI":"10.31449\/inf.v49i37.9896","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:01:40Z","timestamp":1768564900000},"source":"Crossref","is-referenced-by-count":0,"title":["Integration of Multiscale Fusion and Cross-scale Attention Refinement for Enhanced Target Detection Using MSFNet"],"prefix":"10.31449","volume":"49","author":[{"given":"Xiaofang","family":"Liao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinnan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2025,12,24]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/9896\/6378","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/9896\/6378","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:01:41Z","timestamp":1768564901000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/9896"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,24]]},"references-count":0,"journal-issue":{"issue":"37","published-online":{"date-parts":[[2026,1,11]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v49i37.9896","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12,24]]}}}