STDnet: Exploiting high resolution feature maps for small object detection

Bosquet, Brais; Mucientes, Manuel; Brea, V.M.

doi:10.1016/j.engappai.2020.103615

Cited by 57 publications

(22 citation statements)

References 46 publications

(86 reference statements)

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“…Therefore, the single-stage method, especially the anchor-free model, is becoming more and more popular. For dim and small targets on the sea, STDNet built a regional context network to pay attention to its region of interest and the corresponding context and realized the detection of ships less than 16 × 16 pixel area on 720p videos [14]. DSMV [8] introduced a bi-directional gaussian mixture model on the input multi-frame images with temporal relationship and combined it with the deep detector for ship detection.…”

Section: Target Detectionmentioning

confidence: 99%

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Qiao

Liu

et al. 2021

JMSE

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The primary task of marine surveillance is to construct a perfect marine situational awareness (MSA) system that serves to safeguard national maritime rights and interests and to maintain blue homeland security. Progress in maritime wireless communication, developments in artificial intelligence, and automation of marine turbines together imply that intelligent shipping is inevitable in future global shipping. Computer vision-based situational awareness provides visual semantic information to human beings that approximates eyesight, which makes it likely to be widely used in the field of intelligent marine transportation. We describe how we combined the visual perception tasks required for marine surveillance with those required for intelligent ship navigation to form a marine computer vision-based situational awareness complex and investigated the key technologies they have in common. Deep learning was a prerequisite activity. We summarize the progress made in four aspects of current research: full scene parsing of an image, target vessel re-identification, target vessel tracking, and multimodal data fusion with data from visual sensors. The paper gives a summary of research to date to provide background for this work and presents brief analyses of existing problems, outlines some state-of-the-art approaches, reviews available mainstream datasets, and indicates the likely direction of future research and development. As far as we know, this paper is the first review of research into the use of deep learning in situational awareness of the ocean surface. It provides a firm foundation for further investigation by researchers in related fields.

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Section: Target Detectionmentioning

confidence: 99%

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Qiao

Liu

et al. 2021

JMSE

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“…NPAE not only optimizes the image, but also changes the coordinates of the bounding boxes P1 and P2. The modified P1 new and P2 new can be calculated from equation (6).…”

Section: Non-pedestrian Area Estimation For Multiple Imagesmentioning

confidence: 99%

“…High-resolution (HR) image is required to detect pedestrian targets at long distances. HR image has many advantages, such as improved detection results [6]. It is also a basic need for autonomous driving [7].…”

Section: Introductionmentioning

confidence: 99%

Accelerate High Resolution Image Pedestrian Detection With Non-Pedestrian Area Estimation

et al. 2021

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“…In addition, this author also proposed a rotational Libra R-convolutional neural network (CNN) method for balancing the feature, sample, and objective level of a neural network, which achieves state-of-the-art accuracy on the DOTA dataset [ 24 ]. Bosquet et al [ 25 ] proposed STDnet, a convolutional neural network focused on the detection of small objects that are defined as those under 16 pixels × 16 pixels, the authors proposes a novel early visual attention mechanism called the region context network (RCN), which processes only the most promising regions and discards the rest of the input image, allowing the STDnet to keep high resolution feature maps in deeper layers providing low memory consumption and higher frame rates.…”

Section: Related Studiesmentioning

confidence: 99%

Small-Target Complex-Scene Detection Method Based on Information Interworking High-Resolution Network

2021

Sensors

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The CNN (convolutional neural network)-based small target detection techniques for static complex scenes have been applied in many fields, but there are still certain technical challenges. This paper proposes a novel high-resolution small-target detection network named the IIHNet (information interworking high-resolution network) for complex scenes, which is based on information interworking processing technology. The proposed network not only can output a high-resolution presentation of a small target but can also keep the detection network simple and efficient. The key characteristic of the proposed network is that the target features are divided into three categories according to image resolution: high-resolution, medium-resolution, and low-resolution features. The basic features are extracted by convolution at the initial layer of the network. Then, convolution is carried out synchronously in the three resolution channels with information fusion in the horizontal and vertical directions of the network. At the same time, multiple utilizations and augmentations of feature information are carried out in the channel convolution direction. Experimental results show that the proposed network can achieve higher reasoning performance than the other compared networks without any compromise in terms of the detection effect.

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STDnet: Exploiting high resolution feature maps for small object detection

Cited by 57 publications

References 46 publications

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Accelerate High Resolution Image Pedestrian Detection With Non-Pedestrian Area Estimation

Small-Target Complex-Scene Detection Method Based on Information Interworking High-Resolution Network

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