Performance Evaluation of Visual Object Detection for Moving Vehicle

Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.

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Section: Introductionmentioning

confidence: 99%

Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection

et al. 2021

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“…In order to meet the above needs and solve the technical difficulties of UAV target detection, in recent years, researchers have carried out a series of related research. Traditional UAV image target detection methods include frame difference method, background subtraction method, sliding window-based feature extraction algorithm [12], meanshift algorithm, and edge detection algorithm, and recently, deep learning methods are proposed, for examples, fast deep neural networks with knowledge guided training and predicted regions of interests [13], small unmanned aerial vehicle [14], object-based hierarchical change detection [15], application of unmanned aerial vehicles [16], and real-time implementation using GPUs [17]. Traditional sliding window-based features are usually artificially designed Histogram of Oriented gradient features (HoG) [18], Scaleinvariant feature transform features (SIFT) [19], Haar-like wavelet features [20], etc.…”

Section: Introductionmentioning

confidence: 99%

Joint Channel Pruning and Quantization‐Based CNN Network Learning with Mobile Computing‐Based Image Recognition

Liu

Luo

Shao

et al. 2021

Wireless Communications and Mobile Computing

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The development of the Internet and communication technology has ushered in a new era of the Internet of Things (IoT). Moreover, with the rapid development of artificial intelligence, objects are endowed with intelligence, such as home automation and smart healthcare, which are typical applications of artificial intelligence technology in IoT. With the rise of convolutional neural network (CNN) in the field of computer vision, more and more practical applications need to deploy CNN on mobile devices. However, due to the large amount of CNN computing operations and the large number of parameters, it is difficult to deploy on ordinary edge devices. The neural network model compression method has become a popular technology to reduce the computational cost and has attracted more and more attention. We specifically design a small target detection network for hardware platforms with limited computing resources, use pruning and quantization methods to compress, and demonstrate in VOC dataset and RSOD dataset on the actual hardware platform. Experiments show that the proposed method can maintain a fairly accurate rate while greatly speeding up the inference speed.

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Performance Evaluation of Visual Object Detection for Moving Vehicle

Cited by 2 publications

References 14 publications

Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection

Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection

Joint Channel Pruning and Quantization‐Based CNN Network Learning with Mobile Computing‐Based Image Recognition

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