An improved YOLOv11 model is used for vehicle target detection and an Inner-EIoU loss function is introduced during the training phase to improve bounding box regression accuracy and training stability.
In response to the problems of easy missed detection, false detection, and limited detection performance in multi-scale and small target scenes of UAVs in complex backgrounds at high altitudes, this paper proposes a lightweight target detection model SRIN-YOLO based on YOLOv11n. In order to enhance the multi-scale feat...
In this method, the Coordinate Attention module is introduced into the YOLOv8s backbone network to reconstruct the C2f structure and enhances its capability to extract key features, suppressing complex background interference while maintaining its lightweight characteristics.
SFC-YOLO is proposed, an accuracy-enhanced and parameter-efficient framework based on YOLO11n that improves detection accuracy with fewer trainable parameters at the cost of higher theoretical computation.
To address the issues of complex background interference, similar target misdetection and low recognition accuracy of tiny objects in Unmanned Aerial Vehicle (UAV) object detection scenarios, this study proposes an enhanced DEIM-based method for target detection in complex field environments. First, the backbone networ...
To meet the computational constraints of edge devices, an L1 regularization–based channel pruning strategy was applied to batch normalization layers, reducing model complexity while maintaining detection performance, and to improve bounding box regression accuracy in densely occluded scenarios, the Multi-Precision Dist...
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