인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
We propose a framework that integrates a lightweight self-reconstruction-based feature enhancement module into the real- time detector YOLOv26) for improved small object detection. The self-reconstruction-based approach enhances feature representations of small objects by reconstructing the input image from the feature maps and utilizing the difference map between the reconstructed and original images as spatial prior information for small object localization. However, the computational overhead introduced by reconstructing the image up to the original resolution makes it difficult to apply directly this approach to real-time detectors. The purpose of the reconstructed image is not the achievement of perfect image restoration but the identification of the locations of information loss of small objects through the use of the difference map. Based on this analysis, we design a lightweight reconstruction head that generates difference maps at a lower resolution by reducing the number of upsampling operations and integrate it into YOLOv26, a state-of-the-art real-time detector. We also isolate the reconstruction loss gradient from the detection pathway, ensuring that the backbone and neck are optimized solely by the detection loss. Based on experiments using the VisDrone2019 dataset, we demonstrate that the proposed method achieves respective 𝑚𝐴𝑃<sub>50</sub>and 𝑚𝐴𝑃5<sub>0:95</sub> improvements over YOLOv26-N equal to 13.04% and 7.20%, while reducing inference time by 41.3% compared with that of the model integrating the existing self-reconstruction-based approach with comparable detection performance. Our proposed method provides a more efficient and suitable framework for integrating self-reconstruction-based feature enhancement into real-time object detectors, enabling its benefits to be utilized without substantial computational overhead
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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