This study presents SEII-YOLO, a lightweight architecture optimized based on the YOLOv11n framework, which achieves notable performance gains—particularly in distinguishing similar-looking equipment—while reducing parameters by 8.96% and computational load by 3.17%.
The YOLOv12 network is adopted as the baseline model and the ADown module is introduced to improve downsampling efficiency while maintaining lightweight performance, and the BN-CGLU is incorporated into the A2C2f module to enhance the model’s nonlinear representation capability.
Ao-Bo Yue, Puchun Chen, Yan Yang· Journal of Real-Time Image P...· 0 citations
The proposed framework provides a lightweight and reliable solution for multi-object detection in complex industrial environments and supports the development of intelligent visual monitoring systems for smart manufacturing.
Y.-X. Li, H. Chen, L. Zhou· Advances in Production Engin...· 0 citations
The widespread deployment of drone-based optical sensor systems in environmental monitoring, traffic management, and security surveillance has made small object detection a critical task in aerial sensing. However, the sensor-captured objects are typically small, low-resolution, and frequently occluded, posing challeng...
Yong Gao, Hui Jin, Shi-Qing Lu et al.· IEEE Sensors Journal· 0 citations
A novel model based on DETR–the Small Object Enhancement Mechanism Detection Transformer (SOEM-DETR) is proposed, introducing a multi-scale enhanced feature pyramid architecture that efficiently fusing multi-scale features through the Cross-Stage Small Object Enhancement Module (CSP-SOEM).
L. Peng, Shao-Kai Zheng, Dao-Lei Wang· Journal of Real-Time Image P...· 0 citations
The proposed framework provides an efficient solution for lightweight perception in V2X systems and offers valuable technical references for intelligent electromagnetic sensing and wireless propagation-aware visual perception applications.
F. Luo, X. Qiu· Advanced Electromagnetics· 0 citations
A novel model is introduced by assessing the impacts of several YOLO object detection algorithms with the Convolutional Block Attention Module (CBAM) on aircraft detection from satellite images to demonstrate that attention mechanisms have a significant impact when used with the YOLO architecture for object detection i...
Ibrahim Aruk, Hakan Açıkgöz, Ertuğrul Doğruluk· Konya Journal of Engineering...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
