Remote sensing image object detection has advanced significantly, yet small object detection remains challenging due to their limited size and varying scales. Furthermore, real-world deployment often requires algorithms optimized for fewer parameters and faster inference. To address these issues, we propose SMN-YOLO, a lightweight small object detector based on YOLOv8n. Our approach introduces spatial-channel decoupling downsampling to reduce model size while retaining crucial downsampling information. We also present LEFPN, a lightweight multi-scale feature fusion network incorporating Coordinate Attention to capture spatial location cues, enhancing small object detection. Additionally, a multi-scale feature attention module further strengthens feature representation. To improve accuracy, we integrate N-CIoU bounding box regression loss, which minimizes the impact of positional changes on IoU, helping the model focus on low IoU objects. Experimental results on the VEDAI and AI-TOD datasets show that SMN-YOLO outperforms baseline models with a 3.2% and 2.9% improvement in mAP@0.5, respectively, while significantly reducing parameters and only slightly increasing inference time. The proposed model achieves a strong balance between performance and complexity, surpassing several leading detection models.

Improving the detection of small objects in remote sensing is essential for its extensive use in various applications. The diminutive size of these objects, coupled with the complex backgrounds in remote sensing images, complicates the detection process. Moreover, operations like downsampling during feature extraction can cause a significant loss of spatial information for small objects, adversely affecting detection accuracy. To tackle these issues, we propose ESL-YOLO, which incorporates feature enhancement, fusion, and a local attention pyramid. This model includes: (1) an innovative plug-and-play feature enhancement module that incorporates multi-scale local contextual information to bolster detection performance for small objects; (2) a spatial-context-guided multi-scale feature fusion framework that enables effective integration of shallow features, thereby minimizing spatial information loss; and (3) a local attention pyramid module aimed at mitigating background noise while highlighting small object characteristics. Evaluations on the publicly accessible remote sensing datasets AI-TOD and DOTAv1.5 indicate that ESL-YOLO significantly surpasses other contemporary object detection frameworks. In particular, ESL-YOLO enhances mean average precision mAP by 10% and 1.1% on the AI-TOD and DOTAv1.5 datasets, respectively, compared to YOLOv8s. This model is particularly adept at small object detection in remote sensing imagery and holds significant potential for practical applications.

Target detection in UAV images is of great significance in fields such as traffic safety, emergency rescue, and environmental monitoring. However, images captured by UAVs usually have multi-scale features, complex backgrounds, uneven illumination, and low target resolution, which makes target detection in UAV images very challenging. To tackle these challenges, this paper introduces SPDC-YOLO, a novel model built upon YOLOv8. In the backbone, the model eliminates the last C2f module and the final downsampling module, thus avoiding the loss of small target features. In the neck, this paper proposes a novel feature pyramid, SPC-FPN, which employs the SBA (Selective Boundary Aggregation) module to fuse features from two distinct scales. In the head, the P5 detection head is eliminated, and a new detection head, Dyhead-DCNv4, is proposed, replacing DCNv2 in the original Dyhead with DCNv4 and utilizing three attention mechanisms for dynamic feature weighting. In addition, the model uses the CGB (Context Guided Block) module for downsampling, which can learn and fuse local features with surrounding contextual information, and the PPA (Parallelized Patch-Aware Attention) module replacing the original C2f module to further improve feature expression capability. Finally, SPDC-YOLO adopts EIoU as the loss function to optimize target localization accuracy. On the public dataset VisDrone2019, the experimental results show that SPDC-YOLO improves mAP₅₀ by 3.4% compared to YOLOv8n while reducing the parameters count by 1.03 M. Compared with other related methods, SPDC-YOLO demonstrates better performance.

Remote sensing images play a vital role in domains including environmental monitoring, agriculture, and autonomous driving. However, the detection of targets in remote sensing images remains a challenging task. This study introduces innovative methods to enhance feature extraction, feature fusion, and model optimization. The Adaptive Selective Feature Enhancement Module (AFEM) dynamically adjusts feature weights using GhostModule and sigmoid functions, thereby enhancing the accuracy of small target detection. Moreover, the Adaptive Multi-scale Convolution Kernel Feature Fusion Module (AKSFFM) enhances feature fusion through multi-scale convolution operations and attention weight learning mechanisms. Moreover, our proposed ARSOD-YOLO optimized the network architecture, component modules, and loss functions based on YOLOv8, enhancing outstanding small target detection capabilities while preserving model efficiency. We conducted experiments on the VEDAI and AI-TOD datasets, showcasing the excellent performance of ARSOD-YOLO. Our algorithm achieved an mAP50 of 74.3% on the VEDAI dataset, surpassing the YOLOv8 baseline by 3.1%. Similarly, on the AI-TOD dataset, the mAP50 reached 47.8%, exceeding the baseline network by 6.1%.

Aircraft detection technology plays a vital role in civilian applications, with significant attention being devoted to research on related algorithms in recent years. However, most existing research predominantly focuses on aircraft detection from a single top–down viewpoint, which constrains the applicability of detection technology across diverse scenarios. To overcome this limitation, we propose RMVAD-YOLO, a multi-view aircraft detection model built upon YOLOv8. First, we propose a novel Robust Multi-Link Scale Interactive Feature Pyramid Network (RMSFPN), which robustly extracts features of the same aircraft category from multiple views while enhancing feature differentiation between different aircraft categories. Second, we propose the Shared Convolutional Dynamic Alignment Detection Head (SCDADH), which enhances task interaction and collaboration by sharing convolutions between the classification and localization branches while simultaneously reducing the number of parameters, enhancing the model’s ability to deal with multi-scale targets. Additionally, to further leverage background information and enhance the model’s adaptability to multi-scale target variations, we incorporate the LSK Module into the backbone network. Finally, we propose the WFMIoUv3 loss function, which strengthens the model’s focus on challenging samples and improves detection robustness. Experimental results on the newly released Multi-Perspective Aircraft Dataset (MAD) demonstrate that RMVAD-YOLO achieves an accuracy of 90.1%, a recall of 76%, 84.8% mAP@0.5, and 70.5% mAP@0.5:0.95, while reducing parameters and delivering an overall improvement in detection performance compared to the baseline YOLOv8n. RMVAD-YOLO also performed well on the VisDrone 2019 dataset, further demonstrating its reliable generalization capabilities.

Object detection in aerial remote sensing images is challenging due to small target size, low resolution, and frequent occlusions. To address these issues, this paper proposes SDWS-YOLO, an enhanced model based on YOLOv8. In the backbone, the original C2f modules are replaced with a novel Star operation and Depthwise Separable convolution (SDWS) module, which utilizes three distinct operations to strengthen feature representation. The final C2f module and the last downsampling layer are removed to minimize small object feature loss. For the neck, a Four-FPN structure is introduced, integrating multi-scale features via straightforward channel concatenation. The P5 detection head is eliminated to reduce parameters and computational cost. Furthermore, a composite WFS-IoU loss (WiseIoU-FocalerIoU-ShapeIoU) is adopted to improve localization accuracy. Experiments on the VisDrone2019 and DIOR datasets show that SDWS-YOLO improves mAP50 by 2.9% and 1.3%, respectively, over YOLOv8, while having 1.9 million fewer parameters and lower complexity, achieving a superior performance-efficiency balance.