AD-YOLO: adaptive framework for semi-supervised object detection in real-time traffic scenes

AS-YOLOR: An Improved YOLO Model for Small Object Detection in Aerial Images

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.

Semantic Consistency and Uncertainty-Driven Small-Object Detection for Class Imbalance

In aerial image small-object detection, complex imaging perspectives, arbitrary object orientations, and long-tailed category distributions jointly exacerbate sample imbalance, which significantly degrades detection stability and leads to frequent misclassification of minority categories. To address these challenges, this paper proposes a novel training framework termed SCUD. Specifically, in the label noise suppression strategy (LNSS), a contrastive learning mechanism based on semantic consistency is introduced to constrain the aggregation of similar samples in the feature space, thereby reducing the adverse impact of noisy samples on model optimization. In addition, a scale-aware resampling strategy (SARS) is designed to alleviate noise amplification and overfitting caused by excessive repetition of small objects during training. Furthermore, an adaptive instance selection mechanism (AISM) is developed by jointly modeling prediction uncertainty and global statistical priors, enabling the model to dynamically emphasize learning from informative samples. Extensive experiments are conducted on two publicly available unmanned aerial vehicle (UAV) aerial image datasets to validate the effectiveness of the proposed approach. The proposed method achieves an mAP50 of 70.7% on the DOTA-v1.0 dataset and 88.1% on the DIOR dataset. Notably, the detection accuracy of several rare categories is significantly improved, further demonstrating the effectiveness of the proposed method in addressing sample imbalance in aerial image small-object detection.

HASO-DETR: hybrid attention small object detection based on RT-DETR

In unconstrained environments such as aerial and surveillance imagery, achieving precise detection of small objects continues to pose significant challenges in computer vision because of constrained pixel data and cluttered backgrounds, and high sensitivity to bounding box localization errors. Although existing detectors have made progress, they often suffer from the gradual loss of fine-grained features in deep networks and inefficient multiscale feature fusion. Motivated by these challenges, this study designs the HASO-DETR small object detection algorithm.The proposed framework features three key contributions: 1 The Hybrid Dilated Residual Attention Block (HDRAB) embedded in the backbone network, which employs multi-scale dilated convolutions and channel attention mechanisms to enhance and preserve critical features of small objects during downsampling stages; 2 A redesigned cross-scale feature fusion module, CCFM-S2, which utilizes the SPD-Conv operator for informationpreserving downsampling and explicitly integrates high-resolution shallow features (S2 layer), thereby infusing indispensable spatial details into the feature hierarchy for small targets; 3 The adoption of Employing the NormalizedWasserstein Distance (NWD) as a regression loss, we model bounding boxes as Gaussian distributions to effectively reduce sensitivity to minor deviations in bounding box coordinates. Extensive experiments on challenging benchmark datasets VisDrone2019 , demonstrate that HASO-DETR sets a new state-of-the-art performance, outperforming numerous mainstream detectors.

CAM-YOLO: A Framework for Non-Salient Object Detection by Exploiting Contextual Interdependencies

Real-time object detection requires identifying objects in video streams or consecutive images with minimal latency, yet it continues to struggle with non-salient objects—those that are small, occluded, or otherwise inconspicuous. To address this limitation, this paper proposes CAM-YOLO, an enhanced architecture based on YOLOv8. First, to mitigate the baseline model’s limited representational capacity for non-salient targets, we introduce a Multi-Scale Aggregation Module (MSAM) into the feature fusion process, enabling the backbone network to extract more discriminative fine-grained features. Second, to better capture global contextual relationships associated with such objects, we design a Contextual Association Module (CAM) that explicitly models long-range spatial dependencies. Furthermore, we integrate a Dual-Branch Attention Mechanism (DBAM) to refine the feature processing flow, thereby strengthening the contextual feature representations crucial for detecting non-salient instances. Extensive experiments on two large-scale public benchmarks, Microsoft Common Objects in Context 2017 (MS COCO 2017) and PASCAL Visual Object Classes (PASCAL VOC), demonstrate that CAM-YOLO achieves highly competitive performance compared to several widely-adopted realtime detectors.OPEN ACCESS Received: 19/11/2025 Accepted: 15/01/2026 Published: 21/07/2026

Few-shot object detection via dual-feature alignment

YOLO-Net: A lightweight edge-enhanced detection model for small-object recognition in tennis match scenarios

The rapid advancement of deep learning has enabled intelligent analysis in professional sports, yet tennis remains particularly challenging due to small and fast-moving objects, frequent occlusions, and complex backgrounds. To address these difficulties, we propose YOLO-Net, a lightweight detection framework tailored for tennis event analysis. Built upon YOLO11n, the framework integrates three task-oriented improvements: a C3k-MSEIS module for multi-scale edge enhancement and dual-domain feature selection to refine fine-grained boundaries; an ECA channel attention mechanism inserted after C2PSA to strengthen inter-channel dependency modeling and improve feature discriminability; and a Focaler-IoU loss function to emphasize hard and small samples while reducing localization errors. In addition, we construct and annotate a dedicated tennis dataset containing 6,648 images across three categories—player, racquet, and ball—covering diverse scenes, camera angles, and lighting conditions. Experimental results show that YOLO-Net achieves 84.5% precision and 78.2% mAP@0.5 with only 2.58M parameters, outperforming the YOLO11n baseline by 2.5% in precision and 0.9% in mAP while maintaining real-time inference. These findings demonstrate that YOLO-Net is an efficient, accurate, and deployable solution for applications such as referee assistance, tactical analysis, and intelligent broadcasting in tennis competitions.