인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
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.
초록·키워드
The aim of having designated parking spaces for individuals with disabilities is to ensure that only vehicles with proper handicapped signage use them, while preventing unauthorized vehicles from occupying those spaces. To achieve this, real-time monitoring is essential. Existing two-stage object detection models suffer from slow image processing and enhanced backbones with feature pyramid networks are also burdened with expanded parameters. While YOLOv5 model is a compelling choice due to its superior speed and performance compared to existing models. Therefore, this study proposes to make certain modifications to a baseline YOLOv5 model. Instead of the original 9 blocks in the backbone and 4 C3 blocks, we propose to replace them with 6 and 4 EfficientNet blocks, accordingly. These EfficientNet blocks have fewer parameters but still offer higher accuracy in detecting disabled signs, among other types of signs on car windshields. To make up for the reduced number of blocks, we have incorporated an attention mechanism into the proposed architecture before the detection phase. This mechanism enables the model to focus on the crucial regions required for the task. Furthermore, we propose utilizing a more advanced optimizer called AdamW to prevent overfitting. With these enhancements, a novel object detector, attention-based efficient detection model (AED-Net) is proposed. To assess the effectiveness of the proposed approach, we will gather and label a dataset comprising images of cars displaying disabled signage on their windshields. Experiments conducted using this dataset demonstrate that the proposed model achieves a superior F1 score of 0.73 compared to that of baseline model, 0.57. The proposed model utilizes 10 percent fewer parameters compared to the baseline model.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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UCI(KEPA) : I410-ECN-151-24-02-090042072
