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multiscale object detection based on channel and data enhancement at construction sites

MULTIMEDIA SYSTEMS 2023年第1期29卷 49-58页

作者： Wang, Hengyou Song, Yanfei Huo, Lianzhi Chen, Linlin He, Qiang Beijing Univ Civil Engn & Architecture Sch Sci Beijing 100044 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Beijing Univ Civil Engn & Architecture Inst Big Data Modeling & Technol Beijing 100044 Peoples R China

object detection based on computer vision techniques plays an important role in the safety monitoring of large-scene construction sites. However, current object detection algorithms typically have poor performance on small targets. In this study, an enhanced multiscale object detection algorithm is developed to solve the problem of poor detection performance due to scale changes at construction sites. First, a scale-aware data automatic augmentation is defined to learn a data augmentation strategy. Then, to mitigate information loss caused by channel reduction when using feature pyramid network, we propose a method based on subpixel convolution to perform channel enhancement and upsampling, and add a bottom-up path to enhance the complete feature hierarchy with accurate localization signals in the lower layers. Experimental results show that the proposed algorithm achieves better accuracy on the construction site (MOCS) data set and the MS COCO data set. For example, compared with the Faster R-CNN detector with the ResNet-50 backbone network on the MOCS data set and MS COCO data set, the average accuracy increased by 8.0% and 1.5%, respectively. In particular, the average accuracy of small targets increased by 10.3% and 3.4%, respectively.

关键词： multiscale object detection Data enhancement Feature pyramid Subpixel convolution Channel enhancement

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multiscale object detection in Remote Sensing Images Combined with Multi-Receptive-Field Features and Relation-Connected Attention

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REMOTE SENSING 2022年第2期14卷 427页

作者： Liu, Jiahang Yang, Donghao Hu, Fei Nanjing Univ Aeronut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China

object detection is an important task of remote sensing applications. In recent years, with the development of deep convolutional neural networks, object detection in remote sensing images has made great improvements. However, the large variation of object scales and complex scenarios will seriously affect the performance of the detectors. To solve these problems, a novel object detection algorithm based on multi-receptive-field features and relation-connected attention is proposed for remote sensing images to achieve more accurate detection results. Specifically, we propose a multi-receptive-field feature extraction module with dilated convolution to aggregate the context information of different receptive fields. This achieves a strong capability of feature representation, which can effectively adapt to the scale changes of objects, either due to various object scales or different resolutions. Then, a relation-connected attention module based on relation modeling is constructed to automatically select and refine the features, which combines global and local attention to make the features more discriminative and can effectively improve the robustness of the detector. We designed these two modules as plug-and-play blocks and integrated them into the framework of Faster R-CNN to verify our method. The experimental results on NWPU VHR-10 and HRSC2016 datasets demonstrate that these two modules can effectively improve the performance of basic deep CNNs, and the proposed method can achieve better results of multiscale object detection in complex backgrounds.

关键词： convolutional neural networks (CNNs) multi-receptive-field feature extraction multiscale object detection relation-connected attention remote sensing images

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An Anchor-Free Network With Density Map and Attention Mechanism for multiscale object detection in Aerial Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Guo, Yiyou Tong, Xiaohua Xu, Xiong Liu, Sicong Feng, Yongjiu Xie, Huan Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China Tongji Univ Shanghai Key Lab Space Mapping & Remote Sensing Shanghai 200092 Peoples R China Shanghai Inst Intelligent Sci & Technol Shanghai 201203 Peoples R China

Accurate detection of the multiple classes in aerial images has become possible with the use of anchor-based object detectors. However, anchor-based object detectors place a large number of preset anchors on images and regress the target bounding box while anchor-free object detections predict the location of objects directly and avoid the carefully predefined anchor box parameters. object detection in aerial images is faced with two main challenges: 1) the scale diversity of the geospatial objects and 2) the cluttered background in complex scenes. In this letter, to address these challenges, we present a novel Anchor-Free Network with a Density map (DM) and attention mechanism (DA2FNet). Considering the extreme density variations of the detection instances among the different categories in aerial images, the proposed DA2FNet model conducts DM estimation with image-level supervision for the geospatial object counting, to acquire global knowledge about the scale information. A simple and effective image-level global counting loss function is also introduced. In addition, a compositional attention network (AN) is further introduced to enhance the saliency of the foreground objects. The proposed DA2FNet method was compared with the state-of-the-art object detection models, achieving excellent performance on the NWPU VHR-10, RSOD, and DOTA datasets.

关键词： object detection Geospatial analysis Detectors Proposals Training Testing Estimation Aerial images anchor-free attention mechanism density map (DM) multiscale object detection

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Dual-bottleneck feature pyramid network for multiscale object detection

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JOURNAL OF ELECTRONIC IMAGING 2022年第1期31卷 013009-013009页

作者： Chen, Suting Ma, Wenyan Zhang, Liangchen Nanjing Univ Informat Sci & Technol Jiangsu Key Lab Meteorol Observat & Informat Proc Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Wuxi Inst Technol NUIST WIT Nanjing Peoples R China LEQI Technol Shenzhen Peoples R China

multiscale object detection is a challenging task due to the multiscale and multi-classification nature of different objects. Convolutional neural networks are commonly used to extract the features. However, continuous downsampling operations and spatial position quantization result in the loss of detailed information. Subsequently, the high-level feature maps cannot distinguish the features of adjacent objects, and in particular, small objects are ignored. To solve the above problems, we propose a model for multiscale object detection. First, we design a dual-bottleneck subconvolutional network to extract shallow features from the multiscale image pyramid. The subnetwork provides more spatial information for feature maps of the backbone network. Then, we introduce a new downsampling method to avoid losing a large amount of detailed information during the downsampling operations. Finally, we use a loss function to alleviate the foreground-background class imbalance. In the ablation study, we research the matching between the image pyramid module and the backbone network, and we compare different feature fusion modules. We evaluate our proposed model on two publicly available large-scale datasets and observe that our model achieves an 8.5% improvement compared with the classical feature pyramid network and exhibits acceptable detection performance for the occluded and diverse objects. (C) 2022 SPIE and IS&T

关键词： multiscale object detection feature pyramid dual-bottleneck network image pyramid

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ABNet: Adaptive Balanced Network for multiscale object detection in Remote Sensing Imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Liu, Yanfeng Li, Qiang Yuan, Yuan Du, Qian Wang, Qi Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39759 USA

Benefiting from the development of convolutional neural networks (CNNs), many excellent algorithms for object detection have been presented. Remote sensing object detection (RSOD) is a challenging task mainly due to: 1) complicated background of remote sensing images (RSIs) and 2) extremely imbalanced scale and sparsity distribution of remote sensing objects. Existing methods cannot effectively solve these problems with excellent detection accuracy and rapid speed. To address these issues, we propose an adaptive balanced network (ABNet) in this article. First, we design an enhanced effective channel attention (EECA) mechanism to improve the feature representation ability of the backbone, which can alleviate the obstacles of complex background on foreground objects. Then, to combine multiscale features adaptively in different channels and spatial positions, an adaptive feature pyramid network (AFPN) is designed to capture more discriminative features. Furthermore, considering that the original FPN ignores rich deep-level features, a context enhancement module (CEM) is proposed to exploit abundant semantic information for multiscale object detection. Experimental results on three public datasets demonstrate that our approach exhibits superior performance over baseline by only introducing less than 1.5M extra parameters.

关键词： Feature extraction object detection Remote sensing Adaptive systems Task analysis Proposals Detectors Adaptive feature pyramid context exploitation local cross-channel attention multiscale object detection remote sensing image (RSI)

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Attention-Free Global multiscale Fusion Network for Remote Sensing object detection

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

作者： Gao, Tao Li, Ziqi Wen, Yuanbo Chen, Ting Niu, Qianqian Liu, Zixiang Changan Univ Sch Informat Engn Xian 710064 Peoples R China

Remote sensing object detection (RSOD) encounters challenges in complex backgrounds and small object detection, which are interconnected and unable to address separately. To this end, we propose an attention-free global multiscale fusion network (AGMF-Net). Initially, we present a spatial bias module (SBM) to obtain long-range dependencies as a part of our proposal global information extraction module (GIEM). GIEM efficiently captures the global information, overcoming challenges posed by complex backgrounds. Moreover, we propose multitask enhanced structure (MES) and multitask feature pretreatment (MFP) to enhance the feature representation of multiscale targets, while eliminating the interference from complex backgrounds. In addition, an efficient context decoupled detector (ECDD) is presented to provide distinct features for regression and classification tasks, aiming to improve the efficiency of RSOD. Extensive experiments demonstrate that our proposed method achieves superior performance compared with the state-of-the-art detectors. Specifically, AGMF-Net obtains the mean average precision (mAP) of 73.2%, 92.03%, 95.21%, and 94.30% on detection in optical remote sensing images (DIOR), high resolution remote sensing detection (HRRSD), Northwestern Polytechnical University Very High Resolution-10 (NWPU VHR-10), and RSOD datasets, respectively.

关键词： Attention-free multiscale object detection remote sensing images (RSIs) semantic information small object detection

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multiscale Semantic Guidance Network for object detection in VHR Remote Sensing Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Zhu, Shengyu Zhang, Junping Liang, Xuejian Guo, Qingle Harbin Inst Technol Dept Informat Engn Harbin 150001 Peoples R China

With the development of convolutional neural network (CNN), many CNN-based object detection methods have made a remarkable success in very high-resolution (VHR) remote sensing images (RSIs). However, the standard convolution has a fixed receptive field, which makes it deficient in dynamic feature capture;complex backgrounds may also lead to the degradation of detection performance. Accordingly, this letter proposes a novel multiscale semantic guidance network (MSGN) to tackle these problems, wherein, based on the deformable convolution, an improved feature extraction backbone is proposed to capture features dynamically. Moreover, features from different layers are used to ensure the ability for detecting multiscale objects. Furthermore, a multilevel semantic guidance filtering subnetwork is proposed based on the designed backward semantic guidance filtering (BSGF) module, to suppress the complex backgrounds. Experimental results show that the proposed MSGN has stronger robustness and a better accuracy for multiscale object detection, compared with other reference methods.

关键词： Semantics Training Feature extraction Convolution object detection Standards Sports Dynamic feature extraction backbone multilevel semantic guidance filtering subnetwork multiscale object detection very high-resolution (VHR) remote sensing images

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MStrans: multiscale Vision Transformer for Aerial objects detection

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IEEE ACCESS 2022年 10卷 75971-75985页

作者： Lu, Guanlin He, Xiaohui Wang, Qiang Shao, Faming Wang, Jinkang Hao, Likai PLA Army Engn Univ Dept Mech Engn Coll Field Engn Nanjing 210007 Peoples R China

Detecting objects in aerial images is a challenging task due to the large-scale variations and arbitrary orientations with tiny instances. A new multi-scale transformer-based aerial objects detector called MStrans is proposed in this paper to deal with the challenges in aerial detection. To detect remote instances, MStrans adopts a multi-scale patch embedding transformer (MViT) to extract the global features of the image effectively. Furthermore, to capture the different discriminant features for classification and regression branch tasks, the partial interactive fusion module (PIFM) is designed to enhance the semantic expression of the key features of classification and regression tasks by using the strategy of interactive modeling of adjacent layer features. In addition, considering that the transformer may worsen the local feature details while capturing long-distance feature dependencies, this paper designs a global to local interactive fusion module (GLIFM). It uses the advantage of convolution to extract local features to enrich the detailed information in the transformer. Experiments were carried out on DOTA and DIOR datasets, and the MStrans achieves superior detection performances compared with other approaches.

关键词： object detection Transformers Feature extraction Task analysis Detectors Convolution Autonomous aerial vehicles Aerial object detection deep learning multiscale object detection transformer CNN

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Improving Faster R-CNN Framework for multiscale Chinese Character detection and Localization

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2020年第7期E103D卷 1777-1781页

作者： Kim, Minseong Choi, Hyun-Chul Yeungnam Univ Dept Elect Engn 280 Daehakro Gyongsan 38541 Gyeongbuk South Korea

Faster R-CNN uses a region proposal network which consists of a single scale convolution filter and fully connected networks to localize detected regions. However, using a single scale filter is not enough to detect full regions of characters. In this letter, we propose a simple but effective way, i.e., utilizing variously sized convolution filters, to accurately detect Chinese characters of multiple scales in documents. We experimentally verified that our method improved IoU by 4% and detection rate by 3% than the previous single scale Faster R-CNN method.

关键词： Chinese character localization multiscale object detection deep learning

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PCViT: A Pyramid Convolutional Vision Transformer Detector for object detection in Remote-Sensing Imagery

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

作者： Li, Jiaojiao Tian, Penghao Song, Rui Xu, Haitao Li, Yunsong Du, Qian Xidian Univ State Key Lab Integrated Serv Network Xian 710071 Peoples R China Chinese Acad Sci Natl Space Sci Ctr Beijing 100190 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA

Remote-sensing object detection (RSOD) is a fundamental and valuable task in Earth monitoring. However, remote-sensing images (RSIs) are typically acquired from a bird's eye perspective, resulting in intrinsic properties such as complex backgrounds, random and dense distribution of objects, and multiscale objects. These properties hinder the direct application of well-performed detection methods in the natural images (NIs) domain to the RSIs domain, thereby limiting the attainment of desired performance. To address this, we propose a pyramid convolutional vision transformer (PCViT) that gets rid of the limitations of existing transformer methods. First, we employ a pyramid architecture to effectively capture the multiscale information present in RSIs. To enhance the feature extraction capabilities of the transformer, we introduce a parallel convolution module (PCM) that complements the local information that may be missed by the transformer. Furthermore, we propose a self-supervised pretraining strategy called multiperspective pretraining (MPP) to pretrain the model and subsequently finetune it on the downstream detection task. During the finetuning stage, we introduce a local/global ${k}$ -NN attention (LGKA) to improve the token relationship establishment. In the neck part, we propose a feature-reflowing pyramid network (FRPN) to facilitate contextual information interaction and further enhance our PCViT's ability to process multiscale information. Experimental results on two representative datasets, namely NWPU VHR-10 and DIOR, demonstrate the effectiveness of our PCViT, as it achieves outstanding performance. These results highlight the suitability of PCViT for RSOD tasks.

关键词： Transformers Feature extraction Task analysis object detection Detectors Nickel Semantics Convolutional neural network (CNN) feature pyramid network (FPN) multiscale object detection remote-sensing images (RSIs) vision transformer (ViT)

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