Jul 2026· 2026 8th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT)· pp. 758-761· 0 citations· 13 references
Abstract
Small object detection in industrial scenarios faces challenges including limited pixel coverage, weak feature representation, and background interference. To address these problems, this paper presents an improved YOLOv11 detection model. First, a dual-backbone network architecture is designed to simultaneously capture rich semantic information and spatial details through parallel feature extraction paths. Second, the SimAM parameter-free attention mechanism is integrated into top-level feature fusion to adaptively enhance features relevant to small objects. Finally, the Adaptive Spatial Feature Fusion (ASFF) module is improved with a dual attention mechanism to optimize multi-scale feature fusion and mitigate feature conflicts. On a self-constructed industrial tool dataset, the method achieves an mAP@0.5:0.95 of 0.920, improving upon the baseline YOLOv11n by 5.9 percentage points. For small object detection specifically, mAP_s reaches 0.898, representing a 7.9 percentage point improvement. Experiments on the public VisDrone dataset further validate the generalization capability of the approach. Results demonstrate that the proposed method significantly enhances small object detection performance, providing an effective solution for industrial vision applications.
A YOLO11n-based traffic light detection algorithm, named YOLO11n-PRE, which replaces the original C3k2 module in the backbone network with the C3k2-RCB module, which enhances deep feature extraction capability while maintaining lightweight via efficient residual connection and feature recalibration mechanism.
Ce Zheng, Xiao-Qiang Yu, Wenguo Li· International Conference on...· 0 citations
An Adaptive and Scalable YOLO model named AS-YOLOR (Adaptive and Scalable YOLO for Rotated object detection), based on the YOLOv8 baseline is proposed, providing a solution with strong practical potential for achieving efficient and high-precision detection of small, rotated objects.
Jin Huang, Juntao Shen, Min Wang et al.· Applied Sciences· 0 citations
To address the issues of easy loss of small object information during feature fusion, severe complex background interference, and insufficient small object features in low-altitude UAV imagery, an improved YOLOv8s model named ADFPN-YOLOv8s is proposed for small object detection. An Attention-based Dynamic Feature Pyram...
Si-Yi Lu, Jing Zhang, Jian-Wen Huo et al.· IEEE Access· 0 citations
Small and dense object detection remains challenging in complex visual scenes. Repeated downsampling weakens discriminative features of tiny objects, while dense object distributions cause severe feature overlap and semantic ambiguity. To address these challenges, this paper proposes Enhanced Feature-Aware YOLO (EFA-YO...
Zhen Zhang, Xu Xie, Yi Zhang et al.· Engineering Research Express· 0 citations
(1) Objective: Remote sensing object detection faces significant challenges, including complex background interference, large variations in target scales, and insufficient multi-scale feature representation, which often result in missed detections of small objects, inaccurate localization, and inadequate feature fusion...
With the rapid development of UAV technology, UAV image object detection is also facing many challenges, especially for small objects, including dense distribution, feature loss, and background interference. To address these issues, this paper proposes a novel object detection model with dynamic competitive fusion. The...
