Scale Enhancement Pyramid Network for Small Object Detection from UAV Images

Accurate object detection in unmanned aerial vehicle (UAV) images plays a crucial role in fields such as aviation, transportation, and agriculture. However, UAV images often contain a high proportion of small objects, and the limited resources of UAV platforms make it challenging for existing small object detection algorithms to balance detection performance and resource consumption. To address these issues, a lightweight small object detection algorithm called SRP-YOLO is proposed. Compared to the YOLOv8 network, the following improvements are made: first, a high-resolution detection head is designed; second, a receptive field attention convolution module tailored for small UAV objects is introduced to replace the standard convolution module, enhancing the detection capability for small objects and significantly improving their feature representation; Third, partial convolution is combined with C2f to replace some of the standard convolutions, fully leveraging the high-resolution information from shallow features. Finally, A normalized Gaussian Wasserstein Distance (NWD) metric is introduced to reduce the sensitivity of Intersection over Union (IoU) to minor positional deviations of small objects. On VisDrone-DET2019, SRP-YOLO reduces parameters while improving mAP 0.5 by 7.9%. Generalization tests on TinypersonV2 and RAIVD confirm significant gains in small object detection.

With special consideration for complex scenes and densely distributed small objects, this frequently leads to serious false and missed detections for unmanned aerial vehicle (UAV) images in small object detection scenarios. Consequently, we propose a UAV image small object detection algorithm, termed SMA-YOLO. Firstly, a parameter-free simple slicing convolution (SSC) module is integrated in the backbone network to slice the feature maps and enhance the features so as to effectively retain the features of small objects. Subsequently, to enhance the information exchange between upper and lower layers, we design a special multi-cross-scale feature pyramid network (M-FPN). The C2f-Hierarchical-Phantom Convolution (C2f-HPC) module in the network effectively reduces information loss by fine-grained multi-scale feature fusion. Ultimately, adaptive spatial feature fusion detection Head (ASFFDHead) introduces an additional P2 detection head to enhance the resolution of feature maps to better locate small objects. Moreover, the ASFF mechanism is employed to optimize the detection process by filtering out information conflicts during multi-scale feature fusion, thereby significantly optimizing small object detection capability. Using YOLOv8n as the baseline, SMA-YOLO is evaluated on the VisDrone2019 dataset, achieving a 7.4% improvement in mAP@0.5 and a 13.3% reduction in model parameters, and we also verified its generalization ability on VAUDT and RSOD datasets, which demonstrates the effectiveness of our approach.

Less effective information is obtained by the object detection network, due to the small size of the detection object in the entire image, the complex background, and the dense object in unmanned aerial vehicle (UAV) images. In response to the difficulties encountered, a small object detection method in UAV images is proposed as an improved YOLOv5-based algorithm in this paper. First, the space-to-depth(SPD) conv module is introduced into the basic feature extraction network, to improve significant loss of image information during downsampling. Then, various attention mechanisms are added, to intensify the acquisition of regions of interest in UAV images. Finally, the multiscale detection module is improved, to enhance the network's ability to detect small objects in UAV images. By conducting experiments on the VisDrone-DET2019 dataset, the test results of the established model show. The improved algorithm achieved a Mean Average Precision (mAP) of 41.8%, which is 7.8% better than the baseline network. In addition, the detection performance is better than most current mainstream target detection algorithms and is of some practical value.

The fast advancement of unmanned aerial vehicle (UAV) technology has facilitated its use across a wide range of scenarios. Due to the high mobility and flexibility of drones, the images they capture often exhibit significant scale variations and severe object occlusions, leading to a high density of small objects. However, the existing object detection algorithms struggle with detecting small objects effectively in cross-scale detection scenarios. To overcome these difficulties, we introduce a new object detection model, RPS-YOLO, based on the YOLOv8 architecture. Unlike the existing methods that rely on traditional feature pyramids, our approach introduces a recursive feature pyramid (RFP) structure. This structure performs two rounds of feature extraction, and we reduce one downsampling step in the first round to enhance attention to small objects during cross-scale detection. Additionally, we design a novel attention mechanism that improves feature representation and mitigates feature degradation during convolution by capturing spatial- and channel-specific details. Another key innovation is the proposed Localization IOU (LIOU) loss function for bounding box regression, which accelerates the regression process by incorporating angular constraints. Experiments conducted on the VisDrone-DET2021 and UAVDT datasets show that RPS-YOLO surpasses YOLOv8s, with an mAP50 improvement of 8.2% and 3.4%, respectively. Our approach demonstrates that incorporating recursive feature extraction and exploiting detailed information for multi-scale detection significantly improves detection performance, particularly for small objects in UAV images.

With the increasing popularity of Unmanned Aerial Vehicles (UAVs), the accuracy of detecting small objects in large-view images is also expected to increase. However, accurate small object detection is still a challenging problem. Currently, Image Pyramid Network, Feature Pyramid Network (FPN), rich training strategies and data augmentation are widely used to address this problem. To accurately detect small objects, the most important thing is to mine for more feature information. We propose Widened Residual Block (WRB) to break through the bottleneck of residual information gain to extract more feature information. The second is to emphasize or suppress features to prevent small objects from being overwhelmed by a broad background. We introduce an attention mechanism into PANet and propose Enhanced Attention PANet (EA-PANet), which consists of two parts: Context Attention Module (COAM) and Attention Enhancement Module (AEM). COAM outputs attention heatmaps with context, and AEM fuses features from the channel attention module (CAM) and COAM to avoid distraction from a vast background. In addition, we design a lightweight Decoupled Attention Head (DA-head) to dynamically compute important regions for specific tasks and achieve reliable predictions. Experiments show that our method outperforms state-of-the-art (SOTA) detectors. The source code for this work is available at https://github.com/liuxiaolei111/FindSmallObjects.

Unmanned aerial vehicles (UAVs) have been widely used in post-disaster search and rescue operations, object tracking, and other tasks. Therefore, the autonomous perception of UAVs based on computer vision has become a research hotspot in recent years. However, UAV images include dense objects, small objects, and arbitrary object directions, which bring about significant challenges to existing object detection methods. To alleviate these issues, we propose a global-local feature enhanced network (GLF-Net). Considering the difficulty of processing UAV images with complex scenes and dense objects, we designed a backbone based on an involution and self-attention that can extract effective features from complex objects. A multiscale feature fusion module is also proposed to address the presence of numerous small objects in UAV images through multiscale object detection and feature fusion. To accurately detect rotated objects, a rotated regional proposal network was designed based on the midpoint offset representation, which can apply a rotated box to determine the real direction and contour of an object. GLF-Net achieves a state-of-the-art detection accuracy (86.52% mAP) on our created RO-UAV dataset, while achieving 96.95% and 97% mAP on the public datasets HRSC2016 and UCAS-AOU, respectively. The experimental results demonstrate that our method achieves a high detection accuracy and generalization, which can meet the practical requirements of UAVs under various complex scenarios.

Real-time object detection is critical for unmanned aerial vehicles (UAVs) performing various tasks. However, efficiently deploying detection models on UAV platforms with limited storage and computational resources remains a significant challenge. To address this issue, we propose ELNet, an efficient and lightweight object detection model based on YOLOv12n. First, based on an analysis of UAV image characteristics, we strategically remove two A2C2f modules from YOLOv12n and adjust the size and number of detection heads. Second, we propose a novel lightweight detection head, EPGHead, to alleviate the computational burden introduced by adding the large-scale detection head. In addition, since YOLOv12n employs standard convolution for downsampling, which is inefficient for extracting UAV image features, we design a novel downsampling module, EDown, to further reduce model size and enable more efficient feature extraction. Finally, to improve detection in UAV imagery with dense, small, and scale-varying objects, we propose DIMB-C3k2, an enhanced module built upon C3k2, which boosts feature extraction under complex conditions. Compared with YOLOv12n, ELNet achieves an 88.5% reduction in parameter count and a 52.3% decrease in FLOPs, while increasing mAP50 by 1.2% on the VisDrone dataset and 0.8% on the HIT-UAV dataset, reaching 94.7% mAP50 on HIT-UAV. Furthermore, the model achieves a frame rate of 682 FPS, highlighting its superior computational efficiency without sacrificing detection accuracy.

Object detection (OD) in unmanned aerial vehicle (UAV) images faces many challenges, with diverse-scale objects and small objects being particularly prominent issues. To alleviate these challenges, we propose a novel multiscale feature learning and feature fusion network under the guidance of deformable convolution. First, a deformable convolution-guided feature learning (DCGFL) block is designed in the backbone to extract more effective multiscale features. The DCGFL block leverages the adaptability of deformable convolution to the shapes and scales of objects, akin to spatial attention. Moreover, it also employs channel attention to identify important feature maps. Hence, the proposed backbone possesses the functionality of spatial attention and channel attention. Second, in the neck, we devise a simple generalized feature pyramid network (SimpleGFPN) with several deformable convolution-guided feature fusion (DCGFF) blocks to fuse multiscale features. The proposed neck has cross-layer and cross-scale pathways, facilitating effective information exchange and fusion between shallow spatial and deep semantic features. Third, the SIoU loss is used to better model the bounding box regression loss. Finally, experimental results on the VisDrone2021 and UAVDT datasets show that the proposed method outperforms the compared OD methods. In terms of mean average precision, we obtain 37.8% on VisDrone2021 and 18.5% on UAVDT.

The synthetic aperture radar (SAR) has a special ability to detect objects in any climate and weather conditions. Consequently, SAR images are widely used in maritime transportation safety and fishery law enforcement for maritime object detection. Currently, deep-learning models are being extensively used for the detection of objects from images. Among them, the feature pyramid network (FPN) uses pyramids for representing semantic information regardless of the scale and has an improved accuracy of object detection. It is also suitable for the detection of multiple small ship objects in SAR images. This study aims to resolve the problems associated with small-object and multi-object ship detection in complex scenarios e.g., when a ship nears the port, by proposing a detection method based on an optimized FPN model. The feature pyramid model is first embedded in a traditional region proposal network (RPN) and mapped into a new feature space for object identification. Subsequently, the k-means clustering algorithm based on the shape similar distance (SSD) measure is used to optimize the FPN. Initial anchor boxes and tests are created using the SAR ship dataset. Experimental results show that the proposed algorithm for object detection shows an accuracy of 98.62%. Compared with Yolo, the RPN based on VGG/ResNet, FPN based on VGG/ResNet, and other models in complex scenarios, the proposed model shows a higher accuracy rate and better overall performance.

In recent years, research on Unmanned Aerial Vehicles (UAVs) has developed rapidly. Compared to traditional remote-sensing images, UAV images exhibit complex backgrounds, high resolution, and large differences in object scales. Therefore, UAV object detection is an essential yet challenging task. This paper proposes a multi-scale object detection network, namely YOLO-DroneMS (You Only Look Once for Drone Multi-Scale Object), for UAV images. Targeting the pivotal connection between the backbone and neck, the Large Separable Kernel Attention (LSKA) mechanism is adopted with the Spatial Pyramid Pooling Factor (SPPF), where weighted processing of multi-scale feature maps is performed to focus more on features. And Attentional Scale Sequence Fusion DySample (ASF-DySample) is introduced to perform attention scale sequence fusion and dynamic upsampling to conserve resources. Then, the faster cross-stage partial network bottleneck with two convolutions (named C2f) in the backbone is optimized using the Inverted Residual Mobile Block and Dilated Reparam Block (iRMB-DRB), which balances the advantages of dynamic global modeling and static local information fusion. This optimization effectively increases the model’s receptive field, enhancing its capability for downstream tasks. By replacing the original CIoU with WIoUv3, the model prioritizes anchoring boxes of superior quality, dynamically adjusting weights to enhance detection performance for small objects. Experimental findings on the VisDrone2019 dataset demonstrate that at an Intersection over Union (IoU) of 0.5, YOLO-DroneMS achieves a 3.6% increase in mAP@50 compared to the YOLOv8n model. Moreover, YOLO-DroneMS exhibits improved detection speed, increasing the number of frames per second (FPS) from 78.7 to 83.3. The enhanced model supports diverse target scales and achieves high recognition rates, making it well-suited for drone-based object detection tasks, particularly in scenarios involving multiple object clusters.

The effective use of multi-scale features remains an open problem for object detection tasks. Recently, proposed object detectors have usually used Feature Pyramid Networks (FPN) to fuse multi-scale features. Since Feature Pyramid Networks use a relatively simple feature map fusion approach, it can lead to the loss or misalignment of semantic information in the fusion process. Several works have demonstrated that using a bottom-up structure in a Feature Pyramid Network can shorten the information path between lower layers and the topmost feature, allowing an adequate exchange of semantic information from different layers. We further enhance the bottom-up path by proposing a multi-scale residual aggregation Feature Pyramid Network (MSRA-FPN), which uses a unidirectional cross-layer residual module to aggregate features from multiple layers bottom-up in a triangular structure to the topmost layer. In addition, we introduce a Residual Squeeze and Excitation Module to mitigate the aliasing effects that occur when features from different layers are aggregated. MSRA-FPN enhances the semantic information of the high-level feature maps, mitigates the information decay during feature fusion, and enhances the detection capability of the model for large objects. It is experimentally demonstrated that our proposed MSRA-FPN improves the performance of the three baseline models by 0.5–1.9% on the PASCAL VOC dataset and is also quite competitive with other state-of-the-art FPN methods. On the MS COCO dataset, our proposed method can also improve the performance of the baseline model by 0.8% and the baseline model’s performance for large object detection by 1.8%. To further validate the effectiveness of MSRA-FPN for large object detection, we constructed the Thangka Figure Dataset and conducted comparative experiments. It is experimentally demonstrated that our proposed method improves the performance of the baseline model by 2.9–4.7% on this dataset and can reach up to 71.2%.

With the development of unmanned aerial vehicle (UAV) remote sensing technology, target detection based on UAV images has increasingly become a hot spot for research. Aiming at the problems of many small target instances, complex backgrounds and difficult feature extraction in UAV images, we propose a UAV aerial image target detection algorithm called BLUR-YOLO. First, the h-swish activation function is used in the backbone network and the neck network to increase the expressiveness of the model. Second, an attention mechanism (CoordAttention) is added to the bottleneck layer of the backbone network, thereby increasing the weight of valid information and suppressing background noise interference. Finally, by removing redundant nodes of the path aggregation network (PANet), adding additional connections, and using BlurPool instead of the downsampling method, a feature pyramid network (Blur-PANet) is proposed to effectively fuse multilayer features. Experimental results on the VisDrone public dataset show that the proposed drone image object detection model (BLUR-YOLO) is 1.2% better than the YOLOv4 algorithm, which proves the effectiveness of the method.

A pooling-based feature pyramid network for salient object detection

Unmanned Aerial Vehicle (UAV) image object detection plays a crucial role in various fields. However, compared with natural images, UAV images are characterized by significant target scale variations, complex backgrounds, dense small targets, and clustered target distributions, which pose serious challenges to object detection tasks. To address these issues, this study proposes an improved object detection algorithm named MFRA-YOLO, which combines multi-scale feature fusion and receptive-field attention-based convolution, built upon the baseline YOLOv8n algorithm. First, Monte Carlo attention is integrated into receptive-field attention-based convolution to enhance cross-scale information interaction capability, thereby forming a novel convolutional module for downsampling operations. Second, a multi-scale selective fusion module is incorporated into the feature fusion network to enable adaptive cross-scale integration of features. When coupled with the scale sequence feature fusion module, this integration significantly enhances the detection performance for small targets. Finally, the Focaler-PIoUv2 loss function is designed to replace the CIoU in the baseline algorithm. This replacement allows the algorithm to better balance hard and easy samples and improve detection accuracy. Experimental results on the public dataset VisDrone2019 show that MFRA-YOLO achieves superior accuracy-efficiency tradeoffs compared to the baseline YOLOv8n and other YOLO variants. Compared to YOLOv8n, MFRA-YOLO improves mAP50 by 3.5% and mAP50:95 by 2.3%, respectively. These performance gains are accompanied by only modest increases in parameter count and computational cost. Notably, while maintaining performance at 143 FPS, thereby satisfying the real-time deployment requirements for UAVs. Furthermore, compared with several state-of-the-art algorithms designed for UAV scenarios, MFRA-YOLO also offers distinct advantages. To verify the generalization ability and stability of MFRA-YOLO, comparative experiments are carried out on the RSOD dataset, where our algorithm consistently demonstrates excellent detection performance. Overall, these results confirm that MFRA-YOLO not only improves the detection performance for UAV imagery substantially but also achieves an excellent balance between detection accuracy and efficiency.

Object detection on unmanned aerial vehicle (UAV) images has been a hot research topic recently. However, object detection models for general scenarios struggle with UAV images due to the challenges of detecting small targets and handling complex image backgrounds. To solve the two issues, we proposed DPH-YOLOv8, an enhanced version of YOLOv8 tailored for UAV scenarios. First, we improved the prediction head from three prediction heads to double prediction heads (DPHs), reducing the model’s parameters by 42.9% while improving the mAP for small targets. Second, we designed a tiny-path feature fusion (TP-Fusion) module to fuse richer detailed information, enabling the detector to accurately match targets of different sizes and shapes. Third, we introduced a coordinate attention (CA) module to help the model reduce the interference of complex background information and focus on detecting foreground targets. Finally, bottleneck modules were added before the prediction heads to enhance the extraction of small target features. Extensive experimental results on both the VisDrone2021 and UAVDT benchmarks demonstrated that DPH-YOLOv8 not only improved the mAP by 4.5% on VisDrone2021 and 2.2% on UAVDT but also reduced localization error by 0.53%, confusion with objects by 0.69%, and confusion with background by 0.45%. These enhancements, along with a reduction in the model’s parameters, make DPH-YOLOv8 more suitable for UAV scenarios.