RSI-YOLO: Object Detection Method for Remote Sensing Images Based on Improved YOLO

Li, Zhuang; Yuan, Jing; Li, Guixiang; Wang, Hao; Li, Xingcan; Li, Dan; Wang, Xinhua

doi:10.3390/s23146414

Cited by 22 publications

(7 citation statements)

References 33 publications

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“…The approach is analyzed in comparison to six OOD methods, i.e., RetinaNet-O [30], Faster R-CNN [43], S 2 ANet [31], RoI Transformer [14], AOPG [67], and Oriented R-CNN [15], on both RSI datasets. Furthermore, comparisons are made with nine other OOD methods, such as DRN [33], LO-Det-GGHL [75], CenterMap-Net [47], DPGN [51], Oriented RepPoints [5], YOLOv2-O [28], Rep-YOLO [79], RSI-YOLOv5 [80], and YOLOv8-O [81] on the DOTA dataset. The assessment is broadened with an additional four OOD methods, including Double-Heads [40], Gliding Vertex [68], QPDet [53], and DODet [66] on DIOR-R. As shown in Table 3, the DOTA dataset includes 15 different object categories, denoted as PL, BD, BR, GTF, SV, LV, SH, TC, BC, ST, SBF, RA, HA, SP, and HC.…”

Section: Comparison With the Sota Methodsmentioning

confidence: 99%

Task-Aligned Oriented Object Detection in Remote Sensing Images

Qian,

Zhao,

et al. 2024

Electronics

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Oriented object detection (OOD) can recognize and locate various objects more precisely than horizontal object detection; however, two problems have not been satisfactorily resolved so far. Firstly, the absence of interactions between the classification and regression branches leads to inconsistent performance in the two tasks of object detection. Secondly, the traditional convolution operation cannot precisely extract the features of objects in extremely aspect ratio in remote sensing images (RSIs). To address the first problem, the task-aligned detection module (TADM) and the task-aligned loss function (TL) are proposed in this paper. On the one hand, a spatial probability map and a spatial offset map are inferred from the shared features in the TADM and separately incorporated into the classification and regression branches to obtain consistency in the two tasks. On the other hand, the TL combines employing the generalized intersection over union (GIoU) metric with classification loss to further enhance the consistency in the two tasks. To address the second problem, a two-stage detection framework based on alignment convolution (TDA) is proposed. The features extracted from the backbone network are refined through alignment convolution in the first stage, and the final OOD results are inferred from refined features in the second stage. The ablation study verifies the effectiveness of the TADM, TL, and TDA. The comparisons with other advanced methods, on two RSI benchmarks, demonstrate the overall effectiveness of our method.

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Section: Comparison With the Sota Methodsmentioning

confidence: 99%

Task-Aligned Oriented Object Detection in Remote Sensing Images

Qian,

Zhao,

et al. 2024

Electronics

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“…It has been proved that convolutional neural networks have satisfied result on crater detection based on DEM and CCD remote sensing data. For example, the FasterRCNN [21] has already been applied in object detection with higher efficiency compared with previous work,MSA-YOLO [22] is desgined to object detection in DIOR dataset, RSI-YOLOv5 [23] is applied in remote sensing dataset such as DOTA and NWPU-VHR with outperform results, and a Graph Neural Network (GNN) [24] is desgined to execute the semantic segmentation in satellite image data. It can be concluded that deep learning method has great potential in remote sensing data processing.…”

Section: Related Workmentioning

confidence: 99%

A New Lunar Lineament Extraction Method Based on Improved UNet++ and YOLOv5

Yan,

Liang,

Tian

et al. 2024

Sensors

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Lineament is a unique geological structure. The study of Lunar lineament structure has great significance on understanding its history and evolution of Lunar surface. However, the existing geographic feature extraction methods are not suitable for the extraction of Lunar lineament structure. In this paper, a new lineament extraction method is proposed based on improved-UNet++ and YOLOv5. Firstly, new lineament dataset is created containing lineaments structure based on CCD data from LROC. At same time the residual blocks are replaced with the VGG blocks in the down sample part of the UNet++ with adding the attention block between each layer. Secondly, the improved-UNet++ and YOLO networks are trained to execute the object detection and semantic segmentation of lineament structure respectively. Finally, a polygon-match strategy is proposed to combine the results of object detection and semantic segmentation. The experiment result indicate that this new method has relatively better and more stable performance compared with current mainstream networks and the original UNet++ network in the instance segmentation of lineament structure. Additionally, the polygon-match strategy is able to perform preciser edge detail in the instance segmentation of lineament structure result.

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“…Wang et al [7] present the YOLOv7 model to appeal to the object detection fields. Li et al proposed RSI-YOLO (an improved YOLO) for remote sensing [8]. They added the CBAM attention mechanism to the original model.…”

Section: Related Workmentioning

confidence: 99%

SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection

Niu,

Song,

Zhao

2023

Applied Sciences

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Against the backdrop of ongoing urbanization, issues such as traffic congestion and accidents are assuming heightened prominence, necessitating urgent and practical interventions to enhance the efficiency and safety of transportation systems. A paramount challenge lies in realizing real-time vehicle monitoring, flow management, and traffic safety control within the transportation infrastructure to mitigate congestion, optimize road utilization, and curb traffic accidents. In response to this challenge, the present study leverages advanced computer vision technology for vehicle detection and tracking, employing deep learning algorithms. The resultant recognition outcomes provide the traffic management domain with actionable insights for optimizing traffic flow management and signal light control through real-time data analysis. The study demonstrates the applicability of the SE-Lightweight YOLO algorithm, as presented herein, showcasing a noteworthy 95.7% accuracy in vehicle recognition. As a prospective trajectory, this research stands poised to serve as a pivotal reference for urban traffic management, laying the groundwork for a more efficient, secure, and streamlined transportation system in the future. To solve the existing vehicle detection problems in vehicle type recognition, recognition and detection accuracy need to be improved, alongside resolving the issues of slow detection speed, and others. In this paper, we made innovative changes based on the YOLOv7 framework: we added the SE attention transfer mechanism in the backbone module, and the model achieved better results, with a 1.2% improvement compared with the original YOLOv7. Meanwhile, we replaced the SPPCSPC module with the SPPFCSPC module, which enhanced the trait extraction of the model. After that, we applied the SE-Lightweight YOLO to the field of traffic monitoring. This can assist transportation-related personnel in traffic monitoring and aid in creating big data on transportation. Therefore, this research has a good application prospect.

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RSI-YOLO: Object Detection Method for Remote Sensing Images Based on Improved YOLO

Cited by 22 publications

References 33 publications

Task-Aligned Oriented Object Detection in Remote Sensing Images

Task-Aligned Oriented Object Detection in Remote Sensing Images

A New Lunar Lineament Extraction Method Based on Improved UNet++ and YOLOv5

SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection

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