Detection of Small Moving Objects in Long Range Infrared Videos from a Change Detection Perspective

Kwan, Chiman; Larkin, Jude

doi:10.3390/photonics8090394

Cited by 17 publications

(9 citation statements)

References 45 publications

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“…After that, detection is done when the processed pixel value in certain parts of the frame exceeds a detection threshold. [22][23][24] The techniques applied found in literature are, for instance, black-hat filtering, 25 top-hat filtering, 26 and morphological filtration such as erosion and dilation. 23,27 Some authors also apply corner detection 18 or edge detection to find the horizon line and limit the search area for flying targets above the horizon.…”

Section: Algorithms For Optical Detection Of Dronesmentioning

confidence: 99%

“…[22][23][24] The techniques applied found in literature are, for instance, black-hat filtering, 25 top-hat filtering, 26 and morphological filtration such as erosion and dilation. 23,27 Some authors also apply corner detection 18 or edge detection to find the horizon line and limit the search area for flying targets above the horizon. 26 The techniques mentioned so far operate in a frame-by-frame manner, relying on spatial features of the frames.…”

Section: Algorithms For Optical Detection Of Dronesmentioning

confidence: 99%

“…The main advantage of the traditional computer vision algorithms is that they do not require training. 23 Most of the traditional computer vision techniques mentioned are generally fast and can be deployed for real-time applications. However, they do not exploit the specific appearance of the object and may have limitations in recognizing different types of flying objects.…”

Section: Algorithms For Optical Detection Of Dronesmentioning

confidence: 99%

See 2 more Smart Citations

Bio-inspired enhancement for optical detection of drones using convolutional neural networks

Luesutthiviboon,

de Croon,

Altena

et al. 2023

Artificial Intelligence for Security and Defence Applications

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Threats posed by drones urge defence sectors worldwide to develop drone detection systems. Visible-light and infrared cameras complement other sensors in detecting and identifying drones. Application of Convolutional Neural Networks (CNNs), such as the You Only Look Once (YOLO) algorithm, are known to help detect drones in video footage captured by the cameras quickly, and to robustly differentiate drones from other flying objects such as birds, thus avoiding false positives. However, using still video frames for training the CNN may lead to low drone-background contrast when it is flying in front of clutter, and omission of useful temporal data such as the flight trajectory. This deteriorates the drone detection performance, especially when the distance to the target increases. This work proposes to pre-process the video frames using a Bio-Inspired Vision (BIV) model of insects, and to concatenate the pre-processed video frame with the still frame as input for the CNN. The BIV model uses information from preceding frames to enhance the moving target-to-background contrast and embody the target’s recent trajectory in the input frames. An open benchmark dataset containing infrared videos of small drones (< 25 kg) and other flying objects is used to train and test the proposed methodology. Results show that, at a high sensor-to-target distance, the YOLO algorithms trained on BIV-processed frames and concatenation of the BIV-processed frames with still frames increase the Average Precision (AP) to 0.92 and 0.88, respectively, compared to 0.83 when it is trained on still frames alone.

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Section: Algorithms For Optical Detection Of Dronesmentioning

confidence: 99%

Section: Algorithms For Optical Detection Of Dronesmentioning

confidence: 99%

Section: Algorithms For Optical Detection Of Dronesmentioning

confidence: 99%

See 1 more Smart Citation

Bio-inspired enhancement for optical detection of drones using convolutional neural networks

Luesutthiviboon,

de Croon,

Altena

et al. 2023

Artificial Intelligence for Security and Defence Applications

View full text Add to dashboard Cite

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“…In 2020, Chiman Kwan et al [3] combined flow methods with contrast enhancement, connected component analysis, and object association to detect small moving objects in long-range infrared videos. In 2021, Chiman Kwan et al [4] proposed two unsupervised approaches using change detection algorithms for small moving object detection in IR videos. The Result shows that the module can detect objects quite effectively.…”

Section: Related Work a Object Detection Based On Imagesmentioning

confidence: 99%

MS-YOLO: Object Detection Based on YOLOv5 Optimized Fusion Millimeter-Wave Radar and Machine Vision

et al. 2022

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Millimeter-wave radar and machine vision are both important means for intelligent vehicles to perceive the surrounding environment. Aiming at the problem of multisensor fusion, this paper proposes the object detection method of millimeter-wave radar and vision fusion. Radar and camera complement each other, and radar data fusion in machine vision network can effectively reduce the rate of missed detection under insufficient light conditions, and improve the accuracy of remote small object detection. The radar information is processed by mapping transformation neural network to obtain the mask map, so that radar information and visual information in the same scale. A multi-data source deep learning object detection network (MS-YOLO) based on millimeter-wave radar and vision fusion was proposed. Homemade datasets were used for training and testing. This maximized the use of sensor information and improved the detection accuracy under the premise of ensuring the detection speed. Compared with the original YOLOv5 (the fifth version of the You Only Look Once) network, the results show that the MS-YOLO network meets the accuracy requirements better. Among the models, the large model of MS-YOLO has the highest accuracy with an mAP reaching 0.888. The small model of MS-YOLO has good accuracy and speed, and the mAP reaches 0.841 while maintaining a high frame rate of 65 fps.

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“…The applied methods are thus constrained to conventional statistical approaches. [15][16][17] Yet, with the development of small unmanned aerial vehicles expanding the possibilities of today's reconnaissance, there is a high need for more performant, robust, and precise detection techniques.…”

Section: Introduction 11 Motivationmentioning

confidence: 99%

Synthetic thermal imagery for UAV-based reconnaissance by change detection

Strauß,

Bulatov,

Stütz

2023

Artificial Intelligence for Security and Defence Applications

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While deep learning is well-established in image analysis with regard to the visual domain, processing thermal infrared data still mainly relies on conventional methods. Regarding particularly change detection, one may even rely on statistical approaches. The reason beyond is the limited quantity of adequate thermal imagery needed to train deep learning models and the challenging characteristics of thermal imagery itself, which, unlike RGB data, is strongly dependent on the underlying materials and the temporal evolution of environmental conditions as well as scene composition. We therefore aim at generating a new dataset of synthetic thermal imagery which is specifically designed to allow the application of deep learning methods in the area of UAV-based reconnaissance by change detection. In this paper, we present our technical approach to generate this dataset and our preliminary results. We limit the simulated changes within the dataset to two objects of interest, i.e. tanks and landmines, and both object types are rather simplified for preliminary testing. We outline the state-of-the-art methodologies of generating synthetic thermal data and verify them with respect to the requirements, which we deduced from a comprehensive literature review on both deep-learning-based change detection and thermal imagery simulation. We find that the given methods do not fully comply with the requirements on thermal training data. Therefore, two datasets are generated: thermal imagery based on standard methods and thermal imagery that meets the requirements. By comparison, our preliminary findings show significant differences in image features which potentially affect the training of deep learning models.

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Detection of Small Moving Objects in Long Range Infrared Videos from a Change Detection Perspective

Cited by 17 publications

References 45 publications

Bio-inspired enhancement for optical detection of drones using convolutional neural networks

Bio-inspired enhancement for optical detection of drones using convolutional neural networks

MS-YOLO: Object Detection Based on YOLOv5 Optimized Fusion Millimeter-Wave Radar and Machine Vision

Synthetic thermal imagery for UAV-based reconnaissance by change detection

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