R-YOLO: A YOLO-Based Method for Arbitrary-Oriented Target Detection in High-Resolution Remote Sensing Images

Hou, Yongjie; Shi, Gang; Zhao, Yingxiang; Wang, Fan; Jiang, Xian; Zhuang, Rujun; Mei, Yunfei; Ma, Xinjiang

doi:10.3390/s22155716

Cited by 27 publications

(11 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, they did not consider the situation where remote sensing images have low pixel resolution for small objects and complex backgrounds, leading to low detection accuracy, potentially leading to missed detections of small objects. Hou et al [37] integrated the MS transformer module and CBAM into YOLOv5, creating the new R-YOLO network, which advanced the detection performance of remote sensing objects. Huang et al [38]underscored the importance of patch-patch dependence in RSOD and introduced a novel non-local perceptual pyramid named NP-Attention.…”

Section: Object Detection In Remote Sensingmentioning

confidence: 99%

RS-YOLO: A YOLO-Based Method for Small Object Detection in Remote Sensing

Ao,

Zhao,

Zhang

et al. 2024

Preprint

View full text Add to dashboard Cite

Since the objects are usually small and densely distributed, the current mainstream algorithms have poor performance in the task of object detection in remote sensing, and are prone to produce missed detection and false detection. Moreover, remote sensing images(RSI) contain complex background noise, which further exacerbates this phenomenon. To settle these problems, this paper proposes a novel small object detection model in remote sensing, named RS-YOLO. In order to suppress the background noise and enhance the attention to small objects, by integrating Biformer, a dynamic attention mechanism, we construct an improved backbone network. Then, an efficient neck structure based on Gather-and-Distribute(GD) mechanism is introduced to enhance the feature fusion capability of the model. In addition, in order to fully utilize shallow features, we simplify the deep layer structure to reduce its competitiveness in the label assignment process. The strategy simultaneously improves the accuracy and speed of the model. Moreover, we combine Inner-IoU and CIoU loss functions as the new bounding box regression(BBR) loss function, which enhances the localization performance and robustness of the model, and speeds up the training time. Finally, on AI-TOD and DIOR datasets, the mean Average Precision (mAP) of our proposed RS-YOLO reaches 56.3\% and 74.2\% respectively, which exceeds the baseline model and other mainstream algorithms.

show abstract

Section: Object Detection In Remote Sensingmentioning

confidence: 99%

RS-YOLO: A YOLO-Based Method for Small Object Detection in Remote Sensing

Ao,

Zhao,

Zhang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The base networks selected for this study are convolutional neural networks, as other types of networks are not as suitable for extracting features as intended (for example, the YOLO model extracts the rotated rectangle that best fits the object [36]).…”

Section: Popular Convolutional Neural Network Architectures Consideredmentioning

confidence: 99%

Convolutional Neural Networks Adapted for Regression Tasks: Predicting the Orientation of Straight Arrows on Marked Road Pavement Using Deep Learning and Rectified Orthophotography

Cira,

Díaz-Álvarez,

Serradilla

et al. 2023

Electronics

View full text Add to dashboard Cite

Arrow signs found on roadway pavement are an important component of modern transportation systems. Given the rise in autonomous vehicles, public agencies are increasingly interested in accurately identifying and analysing detailed road pavement information to generate comprehensive road maps and decision support systems that can optimise traffic flow, enhance road safety, and provide complete official road cartographic support (that can be used in autonomous driving tasks). As arrow signs are a fundamental component of traffic guidance, this paper aims to present a novel deep learning-based approach to identify the orientation and direction of arrow signs on marked roadway pavements using high-resolution aerial orthoimages. The approach is based on convolutional neural network architectures (VGGNet, ResNet, Xception, and DenseNet) that are modified and adapted for regression tasks with a proposed learning structure, together with an ad hoc model, specially introduced for this task. Although the best-performing artificial neural network was based on VGGNet (VGG-19 variant), it only slightly surpassed the proposed ad hoc model in the average values of the R2 score, mean squared error, and angular error by 0.005, 0.001, and 0.036, respectively, using the training set (the ad hoc model delivered an average R2 score, mean squared error, and angular error of 0.9874, 0.001, and 2.516, respectively). Furthermore, the ad hoc model’s predictions using the test set were the most consistent (a standard deviation of the R2 score of 0.033 compared with the score of 0.042 achieved using VGG19), while being almost eight times more computationally efficient when compared with the VGG19 model (2,673,729 parameters vs VGG19′s 20,321,985 parameters).

show abstract

“…Aiming at the dense distribution of objects and complex detection background in remote sensing images [9] . Hou [10] et alintroduced the convolutional attention mechanism [11] into the YOLOv5 network to improve the detection accuracy of the network for dense objects, but the small target and detailed information is not very good performance.…”

Section: Introductionmentioning

confidence: 99%

Multitarget detection of assembly parts based on improved YOLOv7

Wang,

Hui,

Zhang

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

View full text Add to dashboard Cite

Aiming at multi-target detection in complex human-robot collaborative assembly scenes, an improved YOLOv7 algorithm is proposed. Specifically, the Wise-Intersection over Union(Wise-IoU) loss function and the BiFormer attention module are introduced to improve the recognition performance of small assembly parts. Taking a worm-gear decelerator as an example, a dataset for assembly parts recognition is made. By training the improved network in the self-made dataset, the mAP@.5 value is increased by 3.25 % and the average total loss is reduced by 0.02365. The experiment results show that the improved YOLOv7 algorithm can achieve multi-assembly parts detection in collaborative assembly.

show abstract

R-YOLO: A YOLO-Based Method for Arbitrary-Oriented Target Detection in High-Resolution Remote Sensing Images

Cited by 27 publications

References 34 publications

RS-YOLO: A YOLO-Based Method for Small Object Detection in Remote Sensing

RS-YOLO: A YOLO-Based Method for Small Object Detection in Remote Sensing

Convolutional Neural Networks Adapted for Regression Tasks: Predicting the Orientation of Straight Arrows on Marked Road Pavement Using Deep Learning and Rectified Orthophotography

Multitarget detection of assembly parts based on improved YOLOv7

Contact Info

Product

Resources

About