Enhancing object detection in remote sensing images with improved YOLOv8 model

With the development of remote sensing technology, remote sensing object detection has been widely applied in various fields, but it still faces some thorny challenges, such as the following: 1) the complexity of object scale changes in remote sensing images makes it difficult to improve the performance of small object detection and 2) remote sensing images have complex backgrounds and densely arranged small and weak objects, which pose a serious problem of feature interference. To alleviate these challenges, we propose an end-to-end adaptive feature separation network called AFSNet, which includes a scale-aware module (SAM) and a class-aware module (CAM). The SAM mainly enables feature maps of different resolutions to detect objects of different scales. Shallow feature maps mainly suppress the features of large objects they contain to focus on small object detection, while deep feature maps increase the detailed features of large objects they contain to focus on large object detection. The CAM is mainly used to distinguish the features in the feature map by category, separating the features of different categories into different channels, thus mitigating the problem of inter class feature interference, and blocking background interference. The effectiveness of this article has been proven on the NWPU VHR-10, IPIU-M, DIOR, and DOTA2.0 datasets. It can be widely applied in civilian, military, and other fields. Through experimental verification, our AFSNet achieved 97.70% mAP on the NWPU VHR-10 dataset, 78.9% mAP on the DIOR dataset, and 58.22% mAP on the DOTA2.0 dataset. Our code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Xidian-AIGroup190726/AFSNet</uri>.

With the wide application of remote sensing images (RSIs) in military and civil fields, remote sensing object detection (RSOD) has gradually become a hot research direction. However, we observe two main challenges for remote sensing object detection, namely the complicated background and the small objects issues. Given the different appearances of generic objects and remote sensing objects, the detection algorithms designed for the former usually cannot perform well for the latter. We propose a novel global to local scale-aware detection network (GLSANet) for remote sensing object detection, aiming to solve the above mentioned two challenges. Firstly, we design a global semantic information interaction module (GSIIM) to excavate and reinforce the high-level semantic information in the deep feature map, which alleviates the obstacles of complex background on foreground objects. Secondly, we optimize the feature pyramid network to improve the performance of multiscale object detection in RSIs. Finally, a local attention pyramid (LAP) is introduced to highlight the feature representation of small objects gradually while suppressing the background and noise in the shallower feature maps. Extensive experiments on three public datasets demonstrate that the proposed method achieves superior performance compared with the state-of-the-art detectors, especially on small object detection dataset. Specifically, our algorithm reaches 94.57% mAP on NWPU VHR-10 dataset, 95.93% mAP on RSOD dataset and 77.9% mAP on DIOR dataset, respectively.

Recent studies in remote sensing object detection have made excellent progress and shown promising performance. However, most current detectors only explore rotation-invariant feature extraction but disregard the valuable spatial and semantic prior knowledge in remote sensing images (RSIs), which limits the detection performance when encountering blurred or heavy occluded objects. To address this issue, we propose a mask-reconstruction relation learning (MRRL) framework to learn such prior knowledge among objects and a consistency-reasoning transformer over relation proposals (CTRP) to recognize objects with limited visual features via consistency reasoning. Specifically, MRRL framework applies random mask to some objects in the training dataset and performs masked objects reconstruction to guide the network to learn the distribution consistency of objects. CTRP is the core component of the MRRL framework, which models the interaction between spatial and semantic priors, and uses easy detected objects to reason hard detected objects. The trained CTRP can be integrated into the existing detector to improve the ability of object detection with limited visual features in RSIs. Extensive experiments on widely-used datasets for two distinct tasks, namely remote sensing object detection task and occluded object detection task, demonstrate the effectiveness of the proposed method. Source code is available at https://github.com/sunpeng96/CTRP_mmrotate.

Object detection possesses extremely significant applications in the field of optical remote sensing images. A great many works have achieved remarkable results in this task. However, some common problems, such as scale, illumination, and image quality, are still unresolved. Inspired by the mechanism of cascade attention eagle-eye fovea, we propose a new attention mechanism network named the eagle-eye fovea network (EFNet) which contains two foveae for remote sensing object detection. The EFNet consists of two eagle-eye fovea modules: front central fovea (FCF) and rear central fovea (RCF). The FCF is mainly used to learn the candidate object knowledge based on the channel attention and the spatial attention, while the RCF mainly aims to predict the refined objects with two subnetworks without anchors. Three remote sensing object-detection datasets, namely DIOR, HRRSD, and AIBD, are utilized in the comparative experiments. The best results of the proposed EFNet are obtained on the HRRSD with a 0.622 AP score and a 0.907 AP50 score. The experimental results demonstrate the effectiveness of the proposed EFNet for both multi-category datasets and single category datasets.

Object detection plays a crucial role in scene understanding and has extensive practical applications. In the field of remote sensing object detection, both detection accuracy and robustness are of significant concern. Existing methods heavily rely on sophisticated adversarial training strategies that tend to improve robustness at the expense of accuracy. However, detection robustness is not always indicative of improved accuracy. Therefore, in this paper, we research how to enhance robustness, while still preserving high accuracy, or even improve both simultaneously, with simple vanilla adversarial training or even in the absence thereof. In pursuit of a solution, we first conduct an exploratory investigation by shifting our attention from adversarial training, referred to as adversarial fine-tuning, to adversarial pretraining. Specifically, we propose a novel pretraining paradigm, namely structured adversarial self-supervised (SASS) pretraining, to strengthen both clean accuracy and adversarial robustness for object detection in remote sensing images. At a high level, SASS pretraining aims to unify adversarial learning and self-supervised learning into pretraining and encode structured knowledge into pretrained representations for powerful transferability to downstream detection. Moreover, to fully explore the inherent robustness of vision Transformers and facilitate their pretraining efficiency, by leveraging the recent masked image modeling (MIM) as the pretext task, we further instantiate SASS pretraining into a concise end-to-end framework, named structured adversarial MIM (SA-MIM). SA-MIM consists of two pivotal components, structured adversarial attack and structured MIM (S-MIM). The former establishes structured adversaries for the context of adversarial pretraining, while the latter introduces a structured local-sampling global-masking strategy to adapt to hierarchical encoder architectures. Comprehensive experiments on three different datasets have demonstrated the significant superiority of the proposed pretraining paradigm over previous counterparts for remote sensing object detection. More importantly, regardless of with or without adversarial fine-tuning, it enables simultaneous improvements on detection accuracy and robustness as expected, promisingly alleviating the dependence on complicated adversarial fine-tuning.

Applications of remote sensing images in both defense and civilian sectors have spurred substantial research interest. In the field of remote sensing, object detection confronts challenges such as complex backgrounds, scale diversity, and the presence of dense small objects. To address these issues, we propose an improved deep learning-based model, the Global Multi-scale Fusion Self-calibration Network, which is expected to contribute to alleviating the challenges. It consists of three main components: the hierarchical feature aggregation backbone, which uses improved modules such as the receptive field context-aware feature extraction module, the global information acquisition module, and the simple parameter-free attention module to extract key features and minimize the background interference. To couple multi-scale features, we enhanced the fusing component and designed the multi-scale enhanced pyramid structure integrating the proposed new modules. During the detection phase, especially when focusing on small object detection, we designed a novel convolutional attention feature fusion head. This head is constructed to integrate local and global branches for feature extraction by leveraging channel shuffling and multi-head attention mechanisms for efficient and accurate detection. Experiments on the Detection in Optical Remote Sensing Images (DIOR), Northwestern Polytechnical University Very High-Resolution‐10 (NWPU VHR‐10), remote sensing object detection (RSOD), and DOTAv1.0 data sets show that our method achieves mAP50(mean average precision at 50% intersection over union) of 69.7%, 91.3%, 94.2%, and 70.0%, respectively, outperforming existing comparative methods. The proposed network is expected to provide new perspectives for remote sensing tasks and possible solutions for relevant applications in the image domain.

Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field.

Most current researches primarily focus on improving experimental accuracy using large models, often neglecting the deployment challenges. There is a growing need for lightweight algorithms in certain remote sensing devices. Moreover, remote sensing images (RSIs) often contain numerous small, densely distributed targets, which pose significant detection challenges. To address these issues, we have improved the YOLOv8s network and developed a lightweight remote sensing object detection (RSOD) network based on multiscale features fusion and context information (MFFCI–YOLOv8). This network combines multiscale feature fusion and contextual information to accurately detect objects in RSIs. First, we introduce the lightweight CSP bottleneck with attention module, which utilizes partial convolution calculation and SimAM attention mechanisms to decrease the number of parameters and computational complexity while enhancing feature extraction capabilities. Second, we design the gate spatial pyramid pooling fast module to enhance the model's perception of scale and contextual information, thus improving the detection of small objects. Last, we employ the multiscale fusion lightweight neck module for more efficient multiscale feature fusion, preventing the loss of small objects. Compared to YOLOv8s, our overall model reduces the number of parameters by 7.7% and FLOPs by 11.9%. We validated the accuracy of MFFCI–YOLOv8 on two remote sensing datasets, NWPU VHR-10 and VisDrone. The experimental results demonstrate that our model offers a low computational cost and high detection accuracy compared to other RSOD models and other YOLO models.

High-precision remote sensing image object detection has broad application prospects in military defense, disaster emergency, urban planning, and other fields. However, the arbitrary orientation, dense arrangement, and small size of objects in remote sensing images lead to poor detection accuracy of existing methods. To achieve accurate detection, this paper proposes an arbitrary directional remote sensing object detection method, called FANet, based on feature fusion and angle classification. Initially, the angle prediction branch is introduced, and the circular smooth label method is used to transform the angle regression problem into a classification problem, which solves the difficult problem of abrupt changes in the boundaries of the rotating frame while realizing the object frame rotation. Subsequently, to extract robust remote sensing objects, innovative introduce pure convolutional model as a backbone network, while Conv is replaced by GSConv to reduce the number of parameters in the model along with ensuring detection accuracy. Finally, the strengthen connection feature pyramid network (SC-FPN) is proposed to redesign the lateral connection part for deep and shallow layer feature fusion, and add jump connections between the input and output of the same level feature map to enrich the feature semantic information. In addition, add a variable parameter to the original localization loss function to satisfy the bounding box regression accuracy under different IoU thresholds, and thus obtain more accurate object detection. The comprehensive experimental results on two public datasets for rotated object detection DOTA and HRSC2016 demonstrate the effectiveness of our method.

Remote sensing object detection (RSOD) models play an increasingly important role in modern remote sensing systems. However, during model delivery, sharing, and deployment, RSOD models face increasing risks of unauthorized redistribution, illegal replication, and intellectual property infringement. To mitigate these threats, this paper proposes a white-box watermarking framework for RSOD models that enables reliable copyright verification while preserving the performance of the primary detection task. Specifically, a gradient-based sensitivity analysis of the detection loss is first performed to adaptively identify model parameters that minimally affect detection performance, which are then selected as watermark carriers. Subsequently, a parameter-ranking-based watermark encoding scheme is developed, where watermark bits are embedded by enforcing relative ordering constraints between parameter pairs. To further improve robustness under practical deployment conditions, an attack-simulation-driven training strategy is introduced, in which common perturbations and watermark removal attacks are simulated during the embedding process. In addition, a stealthiness enhancement strategy based on statistical distribution constraints is designed to maintain consistency between the distribution of watermarked parameters and those of the original model, thereby reducing the risk of watermark exposure and localization. Extensive experiments across multiple RSOD datasets and detection architectures demonstrate that the proposed method achieves a high copyright verification success rate with negligible impact on detection accuracy and exhibits strong robustness and stealthiness against a variety of watermark removal attacks.

Object detection in remote sensing imagery presents challenges due to low resolution, complex backgrounds, occlusions, and scale variations, which are critical in disaster response, environmental monitoring, and surveillance. This study proposes a robust object detection framework integrating super-resolution techniques with advanced feature extraction algorithms for remote sensing images. The hybrid model combines Advanced StyleGAN and Swin Transformer. Advanced StyleGAN enhances image resolution, facilitating the detection of small and occluded objects, while Swin Transformer employs hierarchical attention mechanisms for effective feature extraction. Preprocessing techniques, including data augmentation, are incorporated to improve the diversity and accuracy of the training dataset. Evaluation on datasets such as VEDAI-VISIBLE and VEDAI-IR demonstrated exceptional performance, achieving an mAP@0.5 of 97.2%, mAP@0.5:0.95 of 72.8%, and F1-Score of 0.93, with an inference time of 42 ms. The framework maintained robustness under challenging conditions, such as low light and fog, outperforming YOLOv9-S, YOLOv9-E, and DCNN-based methods. Furthermore, it surpassed state-of-the-art models on RSOD and NWPU VHR-10 datasets, achieving superior detection accuracy and robustness. This framework offers a significant advancement in remote sensing object detection, providing an effective solution for complex scenarios. Future work may focus on optimizing computational efficiency and expanding the framework to multimodal or dynamic object detection tasks.

3-D object detection from mobile phones in Device-to-Device (D2D) system provides a new smart payment tool for the next generation of fintech, which is more flexible and efficient than the traditional barcode. In this article, we propose a monocular 3-D object detection method based on depth-guided local convolution. The method combines the information of RGB image mode and depth mode by using a convolution kernel through depth image and works on a single RGB image locally. According to the multiscale input information, the convolution kernel is adaptively adjusted to capture the target objects of different scales, so as to improve the performance of 3-D object detection. In addition, we use the soft-non-maximum suppression algorithm instead of traditional non-maximum suppression to select the best prediction box. In order to further improve the accuracy of 3-D object detection, the depth estimation network and 3-D object detection network are jointly trained in this method to make the two networks constrain each other and achieve the best performance.

Fast and accurate acquisition of the outline of rural buildings on remote sensing images is an efficient method to monitor illegal rural buildings. The traditional object detection method produces useless background information when detecting rural buildings; the semantic segmentation method cannot accurately segment the contours between buildings; the instance segmentation method cannot obtain regular building contours. The rotated object detection methods can effectively solve the problem that the traditional artificial intelligence method cannot accurately extract the outline of buildings. However, the rotated object detection methods are easy to lose location information of small objects in advanced feature maps and are sensitive to noise. To resolve these problems, this paper proposes a two-stage rotated object detection network for rural buildings (TRDet) by using a deep feature fusion network (DFF-Net) and a pixel attention module (PAM). Specifically, TRDet first fuses low-level location and high-level semantic information through the DFF-Net and then reduces the interference of noise information to the network through the PAM. The experimental results show that the mean average precession (mAP), precision, recall rate, and F1 score of the proposed TRDet are 83.57%, 91.11%, 86.5%, and 88.74%, respectively, which outperform the R2CNN model by 15%, 15.54%, 4.01%, and 9.87%. The results demonstrate that the TRDet can achieve better detection in small rural buildings and dense rural buildings.

BiF-DETR:Remote sensing object detection based on Bidirectional information fusion

Object detection is increasingly in demand in IoT service applications. Deep learning based object detection algorithms are now in fashion. As the most popular multi-scale object detection network at present, Feature Pyramid Network achieves feature augmentation by fusing features of neighboring layers. It is widely used in the most advanced object detectors to detect objects of different scales. In this paper, we propose a new attention mechanism guided bidirectional feature pyramid architecture named AgBFPN to enhance the transfer of semantic and spatial information between each feature map. We design Channel Attention Guided Fusion(CAGF) Module and Spatial Attention Guided Fusion(SAGF) Module to enhance feature fusion. The CAGF mitigates the loss of information induced by channel reduction and better transfers the semantic information from high-level to low-level features. The SAGF passes the rich spatial information of shallow features into deep features. Our experiments show that AgBFPN achieves higher Average Precision for multi-scale object detection.