RODFormer: High-Precision Design for Rotating Object Detection with Transformers

Dai, Yaonan; Yu, Jiuyang; Zhang, Dean; Hu, Tianhao; Zheng, Xiaotao

doi:10.3390/s22072633

Cited by 12 publications

(13 citation statements)

References 35 publications

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“…To tackle the challenges posed by the lack of local spatial perceptual field and discontinuous boundary loss in transformer-based rotating target detection, Dai [133] proposes RODFormer, a high-precision model that uses a structured transformer architecture to collect feature information at different resolutions and improve the range of feature information collection. Additionally, a spatial-FFN feedforward network is designed to address the deficiency of FFN in local spatial modeling by fusing local spatial features of depth-separable convolution with the global channel features of multilayer perceptron.…”

Section: Problem Solved Optimization Strategiesmentioning

confidence: 99%

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

Wang

Chen

2023

Preprint

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Small object detection poses a formidable challenge in the field of computer vision, particularly when it comes to analyzing aerial remote sensing images. Despite the rapid development of deep learning and significant progress in detection techniques in natural scenes, the migration of these algorithms to aerial images has not met expectations. This is primarily due to limitations in imaging acquisition conditions, including small target size, viewpoint specificity, background complexity, as well as scale and orientation diversity. Although the increasing application of deep learning-based algorithms to overcome these problems, few studies have summarized the optimization of different deep learning strategies used for small target detection in aerial images. Therefore, this paper aims to explore the application of deep learning methods for small object detection in aerial images. The primary challenges in small object detection in aerial images will be summarized. Next, a meticulous analysis and categorization of the prevailing deep learning optimization strategies employed to surmount the challenges encountered in aerial image detection is undertaken. Following that, we provide a comprehensive presentation of the object detection datasets utilized in aerial remote sensing images, along with the evaluation metrics employed. Additionally, we furnish experimental data pertaining to the currently proposed detection algorithms. Finally, the advantages and disadvantages of various optimization strategies and potential development trends are discussed. Hopefully, it can provide a reference for researchers in this field.

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Section: Problem Solved Optimization Strategiesmentioning

confidence: 99%

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

Wang

Chen

2023

Preprint

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“…The great achievements of transformer in the field of natural language processing have greatly encouraged researchers to explore the role of transformer in the field of computer vision. In recent two years, transformer structure and its variants have been successfully applied to visual tasks such as image classification [44], image captioning [45], object detection [46], and segmentation [47]. The Google team also analyzed the training of vision transformer and provided an effective guidance for future research on visual transformers [48].…”

Section: Transformer-based Architecturesmentioning

confidence: 99%

A Survey on Image Semantic Segmentation Using Deep Learning Techniques

Cheng¹,

Li²,

Li³

et al. 2023

Computers, Materials &Amp; Continua

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Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc. In recent years, due to the remarkable performance of transformer and multilayer perceptron (MLP) in computer vision, which is equivalent to convolutional neural network (CNN), there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture. This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation. Firstly, the commonly used image segmentation datasets are listed. Next, extensive pioneering works are deeply studied from multiple perspectives (e.g., network structures, feature fusion methods, attention mechanisms), and are divided into four categories according to different network architectures: CNN-based architectures, transformer-based architectures, MLP-based architectures, and others. Furthermore, this paper presents some common evaluation metrics and compares the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value on the most widely used datasets. Finally, possible future research directions and challenges are discussed for the reference of other researchers.

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“…Many works have been devoted to investigating high-precision vehicle detection methods by now [ 12 , 13 , 14 , 15 ]. These works mainly focus on the two categories of vehicle detection technology based on traditional image processing technology or convolutional neural networks (CNN).…”

Section: Introductionmentioning

confidence: 99%

A High-Precision Vehicle Detection and Tracking Method Based on the Attention Mechanism

Wang

Dong

Zhao

et al. 2023

Sensors

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Vehicle detection and tracking technology plays an important role in intelligent transportation management and control systems. This paper proposes a novel vehicle detection and tracking method for small target vehicles to achieve high detection and tracking accuracy based on the attention mechanism. We first develop a new vehicle detection model (YOLOv5-NAM) by adding the normalization-based attention module (NAM) to the classical YOLOv5s model. By exploiting the YOLOv5-NAM model as the vehicle detector, we then propose a real-time small target vehicle tracking method (JDE-YN), where the feature extraction process is embedded in the prediction head for joint training. Finally, we present extensive experimental results to verify our method on the UA-DETRAC dataset and to demonstrate that the method can effectively detect small target vehicles in real time. It is shown that compared with the original YOLOv5s model, the mAP value of the YOLOv5-NAM vehicle detection model is improved by 1.6%, while the MOTA value of the JDE-YN method improved by 0.9% compared with the original JDE method.

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RODFormer: High-Precision Design for Rotating Object Detection with Transformers

Cited by 12 publications

References 35 publications

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

A Survey on Image Semantic Segmentation Using Deep Learning Techniques

A High-Precision Vehicle Detection and Tracking Method Based on the Attention Mechanism

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