A Coarse-to-Fine Object Detection Framework for High-Resolution Images with Sparse Objects

This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080.

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Arbitrary-Oriented Object Detection on High Resolution Images Based on Differentiable Architecture Search

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2021.1955666 ◽

2021 ◽

pp. 1-12

Author(s):

Lianyu Cao ◽

Xiaolu Zhang ◽

Zhaoshun Wang

Keyword(s):

High Resolution ◽

Object Detection ◽

High Resolution Images ◽

Oriented Object

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Shadow Detection and Restoration for Hyperspectral Images Based on Nonlinear Spectral Unmixing

Remote Sensing ◽

10.3390/rs12233985 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3985

Author(s):

Guichen Zhang ◽

Daniele Cerra ◽

Rupert Müller

Keyword(s):

High Resolution ◽

Object Detection ◽

Mixture Model ◽

Image Interpretation ◽

Comprehensive Assessment ◽

Spectral Unmixing ◽

Shadow Detection ◽

Hyperspectral Images ◽

Soft Shadow ◽

High Resolution Images

Shadows are frequently observable in high-resolution images, raising challenges in image interpretation, such as classification and object detection. In this paper, we propose a novel framework for shadow detection and restoration of atmospherically corrected hyperspectral images based on nonlinear spectral unmixing. The mixture model is applied pixel-wise as a nonlinear combination of endmembers related to both pure sunlit and shadowed spectra, where the former are manually selected from scenes and the latter are derived from sunlit spectra following physical assumptions. Shadowed pixels are restored by simulating their exposure to sunlight through a combination of sunlit endmembers spectra, weighted by abundance values. The proposed framework is demonstrated on real airborne hyperspectral images. A comprehensive assessment of the restored images is carried out both visually and quantitatively. With respect to binary shadow masks, our framework can produce soft shadow detection results, keeping the natural transition of illumination conditions on shadow boundaries. Our results show that the framework can effectively detect shadows and restore information in shadowed regions.

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VEHICLE DETECTION IN HIGH RESOLUTION IMAGE BASED ON DEEP LEARNING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-49-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 49-54

Author(s):

H. Gao ◽

X. Li

Keyword(s):

High Resolution ◽

Object Detection ◽

Feature Fusion ◽

Single Shot ◽

Feature Maps ◽

Average Precision ◽

Speed Up ◽

High Resolution Images ◽

Small Targets ◽

Fusion Feature

Abstract. Despite its high accuracy and fast speed in object detection, Single Shot Multi-Box Detector (SSD) tends to get undesirable results especially for small targets such as vehicles on high-resolution images. In this paper, we propose a new convolutional neural network based on SSD to detect vehicles on high-resolution images. In the proposed framework, the feature fusion module and detection module are incorporated. In the feature fusion module, feature maps of different scales are integrated into a fusion feature for object detection, which could improve the accuracy effectively. Besides, to prevent the network from overfitting and speed up the training, the batch normalization layer is embedded between the detection layers in the detection module. Some ablation experiments provide strong evidence for the effectiveness of these above structures. On the UCAS-High Resolution Aerial Object Detection Dataset, our network has the ability to achieve the 0.904 AP (average precision) with 0.094 AP higher than SSD512 but similar speed to it.

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Object detection in high resolution images based on multiscale and block processing

Informatics ◽

10.37661/1816-0301-2020-17-2-7-16 ◽

2020 ◽

Vol 17 (2) ◽

pp. 7-16

Author(s):

R. P. Bohush ◽

I. Yu. Zakharava ◽

S. V. Ablameyko

Keyword(s):

Neural Network ◽

High Resolution ◽

Object Detection ◽

Convolutional Neural Network ◽

Image Representation ◽

Input Image ◽

Image Resolution ◽

Detection Accuracy ◽

Block Processing ◽

High Resolution Images

In the paper the algorithm for object detection in high resolution images is proposed. The approach uses multiscale image representation followed by block processing with the overlapping value. For each block the object detection with convolutional neural network was performed. Number of pyramid layers is limited by the Convolutional Neural Network layer size and input image resolution. Overlapping blocks splitting to improve the classification and detection accuracy is performed on each layer of pyramid except the highest one. Detected areas are merged into one if they have high overlapping value and the same class. Experimental results for the algorithm are presented in the paper.

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