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Plug-and-Play Robust aerial object detection Under Hazy Conditions

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 983-998页

作者： Wu, Wei Chang, Hao Chen, Zhiwen Li, Zhu Xidian Univ State Key Lab Integrated Serv Networks Xidian 710071 Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA

object detection under hazy weather conditions is a huge challenge due to the serious obscuration by fog. Recently, to improve detection accuracy in inclement weather, some excellent specifically designed algorithms for foggy conditions have been presented to identify objects from hazy images. However, applied to the detection task in hazy weather, state-of-the-art (SOTA) solutions have to change the architectures and training parameters of existing widely used original object detectors for clear weather, resulting in the waste of resources. To deal with this problem, in this work we propose a plug-and-play aerial object detector under foggy conditions, without the need of altering the structures and retraining the parameters of original task networks at all. The proposed algorithm includes an image enhancement and fusion branch working with a well-known SOTA object detection network for fine weather conditions, where this original detector is completely intact. The novel branch consists of a haze feature elimination module and an object feature restoration module, which are developed to remove fog information and recover clean object feature pyramid, respectively. Moreover, we also propose a detection feature pyramid loss function to more accurately restore object features. Experimental results show that the proposed plug-and-play aerial object detection framework performs well in not only fine weather but hazy weather conditions, while remaining the architecture and parameters of the original detection task network unchanged. This proposed flexible framework can be adapted to adverse weather conditions in all situations.

关键词： Meteorology Detectors object detection Feature extraction Task analysis Image restoration Tail aerial object detection clear weather hazy weather training parameters

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DM-YOLOX aerial object detection method with intensive attention mechanism

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JOURNAL OF SUPERCOMPUTING 2024年第9期80卷 12790-12812页

作者： Li, Xiangyu Wang, Fengping Wang, Wei Han, Yanjiang Zhang, Jianyang Xian Polytech Univ Xian 710600 Shaanxi Peoples R China

In aerial image detection, difficulties in feature extraction and low detection accuracy arise due to background interference, occlusion, and the presence of multiple small objects. This paper proposes a DM-YOLOX aerial object target detection method with intensive attention mechanism. Firstly, the proposed approach incorporates coordinate attention (CA) and a dense connection method into the backbone network architecture, enabling adaptive channel weighting throughout the feature extraction process. This facilitates the enhancement of significant features while suppressing less relevant ones, thereby augmenting the network's capacity to represent object features and ensuring retention and reinforcement of key features. Secondly, the multibranch extraction module (MBE) is incorporated into the feature fusion network to enhance the network's ability in extracting multi-scale feature information from images with extensive coverage, thereby enhancing the detection accuracy and efficiency of small- and medium-sized objects in complex scenes. Finally, the utilization of SIoU instead of IoU as the bounding box loss function effectively addresses the issue of mismatch between real and predicted boxes, leading to accelerated network convergence and improved performance during model training. After training and testing on the VisDrone 2019 dataset, this method effectively detects small objects in complex environments. The DM-YOLOX model shows a significant improvement of 2.7% in mAP compared to the baseline network, while achieving an 8% increase in frames per second (FPS).

关键词： aerial object detection YOLOX Attention mechanism SIoU Deep learning

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AODet: aerial object detection Using Transformers for Foreground Regions

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Wang, Xiaoming Chen, Hao Chu, Xiangxiang Wang, Peng Northwestern Polytech Univ Ningbo Inst Sch Comp Sci Xian 710072 Peoples R China Meituan Beijing Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310058 Peoples R China

aerial object detection is an important task and has received significant attention in recent years. aerial images typically depict small and sparse instances against a simple background. Nevertheless, the simple background can only provide limited information. Based on the observation, we present a new transformer-based framework for aerial object detection. In contrast to previous methods that address sparsity through multistage pipelines involving region-of-interest (RoI) techniques or sparse convolutions, our method, referred as AODet, enjoy two significant advantages: 1) AODet is a simple yet accurate object detector which is specialized for aerial object detection. AODet identifies the background regions earlier and then only operates on the regions which most likely include the foreground objects, thereby significantly reducing the redundant computations. The utilization of transformer exploits more context information between foreground regions, helping to retain high-quality detection results and 2) instead of involving the sparse operations like sparse convolutions or clustering algorithms/RoI operations, AODet employs transformer to detect objects from foreground proposals. Our approach is simpler and can be easily implemented with simple tensor manipulations. Extensive experiments have conducted on VisDrone and DOTA. AODet achieves 40.9 AP on Visdrone and 79.6 mAP DOTA, demonstrating the effectiveness of AODet.

关键词： aerial object detection transformer aerial object detection transformer

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MAFPN: a mixed local-global attention feature pyramid network for aerial object detection

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REMOTE SENSING LETTERS 2024年第9期15卷 907-918页

作者： Ma, Tengfei Yin, Haitao Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China

aerial object detection (AOD) aims at locating and categorizing aerial targets in remote sensing image, which has wide practical applications. Mainstream feature pyramid network (FPN) has made great progresses in multi-scale natural objects detection, which is also successfully extended into AOD. However, existing FPNs still have some shortcomings in discriminative feature extraction and fusion for aerial objects due to diverse object scales and dense small objects. To address these limitations, this letter proposes a novel mixed local-global attention FPN (MAFPN). The core components of MAFPN are cross-scale transformer (CST) module and multi-scale spatial fusion (MSSF) module, which are equipped at the feature extraction and fusion of FPN, respectively. Specifically, the CST module is devised to capture global semantic information and cross-scale similarity by exploiting self-attention in multi-scale features. The MSSF module utilizes the spatial attention maps as fusion weights, and adaptively fuses multi-scale features between adjacent two levels. Such mixed attention approach enhances the multi-scale feature learning in dense small objects. Besides, to further balance the influences of different object scales, we propose an area-guided dot-IOU (ADIOU) regression loss, which takes into account the size of objects and enables the model to prioritize the centre position of small objects. Experimental results on public datasets demonstrate the superiority of proposed method.

关键词： aerial object detection feature pyramid network Transformer multi-scale spatial fusion area-guided Dot-IOU loss

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YOLO-MMS for aerial object detection model based on hybrid feature extractor and improved multi-scale prediction

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VISUAL COMPUTER 2024年第7期41卷 4759-4778页

作者： Junos, Mohamad Haniff Khairuddin, Anis Salwa Mohd Univ Sains Malaysia Sch Aerosp Engn Engn Campus Nibong Tebal 14300 Penang Malaysia Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia

object detection in aerial images has become an important research subject due to the widespread use of aerial platforms, including satellites and unmanned aerial vehicles. However, the task is challenging because it involves a complex background, a high number of small objects, and densely distributed objects, leading to poor detection accuracy. Moreover, despite their excellent detection accuracy, existing one-stage object detection methods have complex structures that require huge computational power, generate high parameters, and exhibit slow inference speed, which makes them unsuitable for edge device applications. To address these issues, this paper proposes an accurate and lightweight object detection model named the YOLO-MMS model. The developed model incorporates several improvements, notably the hybrid backbone structure, which integrates a novel Mix-Mobile inverted bottleneck module to optimize efficiency by reducing the number of generated parameters. Additionally, the multi-scale prediction employs small efficient layer aggregation network and spatial pyramid pooling modules to improve feature extraction across multiple scales. Finally, the model includes an additional detection head and utilizes the Swish activation function to enhance detection accuracy. The evaluation results on the VisDrone and VEDAI datasets demonstrate that the proposed YOLO-MMS model achieved superior accuracy compared to other lightweight YOLO-based models. Furthermore, the proposed model showed significant improvements in model size with a reduction of 41.77% compared to its original YOLOv4-tiny model. These findings indicate that the proposed model presents optimal trade-offs in terms of accuracy and efficiency, rendering it exceptionally suitable for real-time applications on embedded systems. Our code is available at: https://***/hanifjunos/YOLO-MMS.

关键词： Lightweight YOLO MixMBConv aerial object detection Deep learning

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Recent Advances for aerial object detection: A Survey

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ACM COMPUTING SURVEYS 2024年第12期56卷 1-36页

作者： Leng, Jiaxu Ye, Yongming Mo, Mengjingcheng Gao, Chenqiang Gan, Ji Xiao, Bin Gao, Xinbo Chongqing Univ Posts & Telecommun Chongqing Peoples R China Chongqing Inst Brain & Intelligence Guangyang Bay Lab Chongqing Peoples R China

aerial object detection, as object detection in aerial images captured from an overhead perspective, has been widely applied in urban management, industrial inspection, and other aspects. However, the performance of existing aerial object detection algorithms is hindered by variations in object scales and orientations attributed to the aerial perspective. This survey presents a comprehensive review of recent advances in aerial object detection. We start with some basic concepts of aerial object detection and then summarize the five imbalance problems of aerial object detection, including scale imbalance, spatial imbalance, objective imbalance, semantic imbalance, and class imbalance. Moreover, we classify and analyze relevant methods and especially introduce the applications of aerial object detection in practical scenarios. Finally, the performance evaluation is presented on two popular aerial object detection datasets VisDrone-DET and DOTA, and we discuss several future directions that could facilitate the development of aerial object detection.

关键词： aerial object detection imbalance deep learning

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PYRAMID MASKED IMAGE MODELING FOR TRANSFORMER-BASED aerial object detection 30

PYRAMID MASKED IMAGE MODELING FOR TRANSFORMER-BASED AERIAL O...

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30th IEEE International Conference on Image Processing (ICIP)

作者： Zhang, Cong Liu, Tianshan Ju, Yakun Lam, Kin-Man Hong Kong Polytech Univ Dept Elect & Informat Engn Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781728198354

Two obstacles, the scarcity of annotated samples and the difficulty in preserving multi-scale hierarchical representations, hinder the advancement of vision Transformer-based aerial object detection. The emergence of self-supervised learning has inspired some solutions to the first issue. However, most solutions focus on single-scale features, conflicting with solving the second issue. To bridge this gap, this paper proposes a novel pyramid masked image modeling (MIM) framework, termed PyraMIM, for self-supervised pretraining in aerial scenarios. Without manual annotation, PyraMIM enables establishing pyramid representations during pretraining, which can be seamlessly adapted to downstream aerial object detection for performance improvement. Experimental results demonstrate the effectiveness and superiority of our method.

关键词： Vision Transformer Masked Image Modeling Self-Supervised Learning Pyramid Architecture aerial object detection

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Rotated aerial object detection Based on Improved YOLOv5 Method

Rotated Aerial Object Detection Based on Improved YOLOv5 Met...

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International Conference on Autonomous Unmanned Systems (ICAUS)

作者： Fan, Yali Chen, Junhai Ma, Zhaowei Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China

ISBN: (纸本)9789819904815;9789819904792;9789819904785

This paper increases the accuracy of rotated aerial objects detection under certain practical application scenario by proposed enriching the data set method. In order to reduce computing time and improve the generalization ability of the model, two selections before and after the data augmentation are designed. Different sizes of data set are used to train and the corresponding results are compared to present the influence of size of data set on the training. The data set with more rotated images achieved 67% after 100 epochs of training on YOLOv5 network with the pre-trained model *** measured by mAP@.5:.95, higher than that of the original size of data set (65.6%) and small size of data (61.5%). The small data set with half of images in the original data set has the smallest value of mAP@.5:.95. Our method is proved to be efficient to solve the detection problem of the UAV when it is circling on the altitude of 100 m and shows the potential of data augmentation on aerial object detection.

关键词： Data augmentation Rotated images aerial object detection

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AN ORIENTATION-AWARE ANCHOR-FREE DETECTOR FOR aerial object detection

AN ORIENTATION-AWARE ANCHOR-FREE DETECTOR FOR AERIAL OBJECT ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Duan, Mudi Meng, Ran Xiao, Liang Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Most aerial object detectors mainly adopt anchor-based methods, which require manually designed rough anchors. However, these anchors usually contain background redundancy unrelated to the objects, decreasing detection performance and introducing additional calculations. In this paper, we propose an orientation-aware anchor-free network (OAF-Net) for object detection. OAF-Net first produces coarse oriented boxes by coarse location network based on pixel-level regression and then refines them with aligned features. In particular, we also apply a new metric to measure the orientation-aware center-ness, which is a weighting strategy of positive samples to reduce the different contributions of the low-quality detection boxes. Experiments on the DOTA1.0 and HRSC2016 datasets reveal that OAF-Net outperforms the state-of-the-art in terms of comprehensive detection performance for various objects with different scales and orientations in aerial images.

关键词： aerial object detection anchor-free feature alignment orientation-aware center-ness

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EllipseIoU: A General Metric for aerial object detection 5th

EllipseIoU: A General Metric for Aerial Object Detection

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5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Yang, Xinbo Yu, Chenglong Ruan, Rui Liu, Lei Chao, Wang Luo, Bin Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Anhui Prov Key Lab Multimodal Cognit Computat Hefei Peoples R China Anhui Univ Sch Internet Hefei Peoples R China

ISBN: (纸本)9783031189128;9783031189135

Existing works in object detection usually adopt different Intersection over Union (IoU) metrics to define the similarity between predicted box and target box, which greatly boost the detection performance. For the task of aerial object detection, since the predicted and target boxes are with different sizes, aspect ratios and orientations, existing IoU metrics can not measure their similarity well. In this work, we propose a new and generic metric for robust aerial object detection, called EllipseIoU, which incorporates the ellipse distance between the predicted box and the target box. In particular, we find the inner ellipse from the box, and then calculate the ellipse distance between predicted and target boxes based on the distances of focal points. We prove that the proposed ellipse distance well measures the relations of sizes, aspect ratios and orientations between predicted and target boxes. Moreover, we also incorporate the relation of short axis length between two boxes to measure the relation of sizes. Based on the ellipse distance, we design the EllipseIoU metric by combining with the IoU metric and integrate it into detectors for performance boosting of aerial object detection. Experimental results on public datasets show that the detector with EllipseIoU significantly improve the baseline without impact on the efficiency and achieves state-of-the-art performance.

关键词： aerial object detection Intersection over Union (IoU)

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