Vehicle Detection and Counting in High-Resolution Aerial Images Using Convolutional Regression Neural Network

Tayara, Hilal; Soo, Kim Gil; Chong, Kil To

doi:10.1109/access.2017.2782260

Cited by 140 publications

(89 citation statements)

References 31 publications

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“…The state-of-the-art detectors [8][9][10][11] show a significant performance gap between low-resolution images and their high-resolution counterparts due to a lack of input features for small objects [12]. In addition to general object detectors, researchers have proposed specialized methods, algorithms, and network architectures to detect particular types of objects from satellite images such as vehicles [13,14], buildings [15], and storage tanks [16]. These methods are object-specific and use fixed resolution for feature extraction and detection.…”

Section: Problem Description and Motivationmentioning

confidence: 99%

“…The RetinaNet [9] uses a focal loss function to deal with the data imbalance problem caused by many background objects and often shows similar performance as the two-stage approaches. There are many works on the usage of deep CNNs to detect and count small objects in remote sensing imagery such as vehicles [13,44,45]. In [13], the authors introduce a convolutional regression neural network to detect vehicles from satellite imagery.…”

Section: Object Detectionmentioning

confidence: 99%

“…There are many works on the usage of deep CNNs to detect and count small objects in remote sensing imagery such as vehicles [13,44,45]. In [13], the authors introduce a convolutional regression neural network to detect vehicles from satellite imagery. Furthermore, a deep CNN-based detector is proposed in [44] to detect multi oriented vehicles from remote sensing imagery.…”

Section: Object Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Rabbi

Ray

Schubert³

et al. 2020

Preprint

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The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.

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Section: Problem Description and Motivationmentioning

confidence: 99%

Section: Object Detectionmentioning

confidence: 99%

Section: Object Detectionmentioning

confidence: 99%

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Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Rabbi

Ray

Schubert³

et al. 2020

Preprint

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“…Counting methods have been mainly applied for counting crowd [5,[28][29][30][31], vehicles [6,32], and cell [7]. In agriculture, there have been limited research for counting apples and oranges [33], tomatoes [8,9], maize tassels [34], and animals [27].…”

Section: Counting Applicationsmentioning

confidence: 99%

“…In addition, several methods based on DNNs [32,[43][44][45] have been developed for object detection and tracking in satellite and aerial imagery, particularly vehicles. For counting and detecting of man-made objects ( such as vehicles in parking lots) in aerial imagery, one deal with the imagery that contain an equal distribution of objects of interest and background and there is not any overlap between objects.…”

Section: Unmanned Aerial Systemsmentioning

confidence: 99%

DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

et al. 2019

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Recent deep-learning counting techniques revolve around two distinct features of data—sparse data, which favors detection networks, or dense data where density map networks are used. Both techniques fail to address a third scenario, where dense objects are sparsely located. Raw aerial images represent sparse distributions of data in most situations. To address this issue, we propose a novel and exceedingly portable end-to-end model, DisCountNet, and an example dataset to test it on. DisCountNet is a two-stage network that uses theories from both detection and heat-map networks to provide a simple yet powerful design. The first stage, DiscNet, operates on the theory of coarse detection, but does so by converting a rich and high-resolution image into a sparse representation where only important information is encoded. Following this, CountNet operates on the dense regions of the sparse matrix to generate a density map, which provides fine locations and count predictions on densities of objects. Comparing the proposed network to current state-of-the-art networks, we find that we can maintain competitive performance while using a fraction of the computational complexity, resulting in a real-time solution.

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A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

Hashemi

Emami

Sangar

2021

Int J Communication

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Summary The vehicle detection and classification (VDC) problem has received much attention recently due to the increased security threats and the need to develop intelligent transportation systems. A large number of approaches have been proposed for the VDC problem using neural networks. To determine how neural networks‐based approaches have developed for the VDC in recent years, this paper surveys the VDC approaches through a literature review with the range Jan. 2012 through Apr. 2021. To do this, we introduce a new comparison framework to classify and compare the VDC approaches. Our proposed framework is composed of nine comparison dimensions: input data type, vehicle type, scale, scope, dynamicity, vehicle detection method, vehicle classification method, application, and evaluation method. Next, using the proposed framework, we discuss the evolution of the VDC approaches and identify several open issues that have emerged in the field. This paper provides a guide for researchers to use or design robust VDC systems with proper characteristics based on their needs.

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Vehicle Detection and Counting in High-Resolution Aerial Images Using Convolutional Regression Neural Network

Cited by 140 publications

References 31 publications

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

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