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.
논문 기본 정보
- 저자정보
초록·키워드
Image recognition technology has seen gradual enhancement due to advancements in deep learning algorithms. Nonetheless, target detection algorithms encounter challenges like low positioning accuracy and missed and false detections in multiple-target, large-scale, and complex environments. Therefore, in order to improve the image recognition processing effect, an adaptive weighted fusion-YOLOv4 object detection algorithm network structure is designed and applied to object detection in virtual reality scenarios. Including the backbone network, neck network, and prediction network. An adaptive feature fusion module is incorporated into the backbone network to enhance the learning capacity of residual blocks. Additionally, a broad-scale cross-stage fusion network is implemented in the neck network to minimize information loss during feature fusion and facilitate more effective utilization of feature information by the neck network. At the same time, the SIoU loss function is used to improve the convergence speed. When applied to natural scenery, urban roads, office environments, and plateau deserts, the Adaptive Weighted Fusion YOLOv4 algorithm achieved mAP scores of 0.9056, 0.9143, 0.9106, and 0.9812, respectively. Compared to other methods, it exhibited the least fluctuation amplitude in the image signal, with maximum amplitude reduction rates at 14.25%, 17.36%, and 22.36%, respectively. This method enhances network fusion and feature extraction, resulting in superior detection accuracy during image recognition processes.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-26-02-094677848
