MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects

Cui, Lisha; Ma, Rui; Lv, Pei; Jiang, Xiaoheng; Gao, Zhimin; Zhou, Bing; Xu, Mingliang

doi:10.48550/arxiv.1805.07009

Cited by 18 publications

(14 citation statements)

References 23 publications

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“…As for the baselines, we only conducted the experiments based on state-of-the-art object detectors. Currently, there is some computer-vision work related to detecting small objects [47][48][49][50]. The core ingredient is the multiscale feature with high resolution to improve detection accuracy [47,48].…”

Section: Discussionmentioning

confidence: 99%

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A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds

et al. 2019

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With the launch of space-borne satellites, more synthetic aperture radar (SAR) images are available than ever before, thus making dynamic ship monitoring possible. Object detectors in deep learning achieve top performance, benefitting from a free public dataset. Unfortunately, due to the lack of a large volume of labeled datasets, object detectors for SAR ship detection have developed slowly. To boost the development of object detectors in SAR images, a SAR dataset is constructed. This dataset labeled by SAR experts was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. It consists of 43,819 ship chips of 256 pixels in both range and azimuth. These ships mainly have distinct scales and backgrounds. Moreover, modified state-of-the-art object detectors from natural images are trained and can be used as baselines. Experimental results reveal that object detectors achieve higher mean average precision (mAP) on the test dataset and have high generalization performance on new SAR imagery without land-ocean segmentation, demonstrating the benefits of the dataset we constructed.

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Section: Discussionmentioning

confidence: 99%

“…Currently, there is some computer-vision work related to detecting small objects [47][48][49][50]. The core ingredient is the multiscale feature with high resolution to improve detection accuracy [47,48]. Considering the relatively small ships, these models can be adapted for ship detection based on our dataset.…”

Section: Discussionmentioning

confidence: 99%

A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds

et al. 2019

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“…At the same time, detecting large objects in shallow layers are also non-optimal without large enough receptive fields. Thus, handling feature were later developed [109,110,109,111,112,92,113,114,115,116,117,118,119], with modifications to the feature pyramid block (see Fig. 8).…”

Section: Feature Representation Learningmentioning

confidence: 99%

Recent advances in deep learning for object detection

2020

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Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades.Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc.Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning.

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“…By design, FMF preserves both spatial and semantic information. Following the SSD idea, Cui et al [126] proposed a Multi-scale Deconvolutional Single Shot Detector (MDSSD), where multiple feature maps at different scales are upsampled to increase the spatial resolution. For better localization of small objects, concatenation is used in [127], instead of summation in the fusion block to preserve more information across layers.…”

Section: Feature Learningmentioning

confidence: 99%

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Rekavandi¹,

Liu²,

Boussaid³

et al. 2022

Preprint

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Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided.

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MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects

Cited by 18 publications

References 23 publications

A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds

A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds

Recent advances in deep learning for object detection

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

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