An Extended Set of Haar-like Features for Bird Detection Based on AdaBoost

Huang, Chih-Cheng; Tsai, Chun-Yi; Yang, Horng-Chang

doi:10.1007/978-3-642-27183-0_17

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…At the time, it was 10-100 times faster than any other approach with similar accuracy [3]. Other examples of objects in which VJ was successfully applied include vehicles [34], pedestrians [35], license plates [36], hands [37], and birds [38].…”

Section: Viola-jones (Vj)mentioning

confidence: 99%

Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection

Santos,

Montalvão,

Araujo

et al. 2024

Sensors

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This paper explores a data augmentation approach for images of rigid bodies, particularly focusing on electrical equipment and analogous industrial objects. By leveraging manufacturer-provided datasheets containing precise equipment dimensions, we employed straightforward algorithms to generate synthetic images, permitting the expansion of the training dataset from a potentially unlimited viewpoint. In scenarios lacking genuine target images, we conducted a case study using two well-known detectors, representing two machine-learning paradigms: the Viola–Jones (VJ) and You Only Look Once (YOLO) detectors, trained exclusively on datasets featuring synthetic images as the positive examples of the target equipment, namely lightning rods and potential transformers. Performances of both detectors were assessed using real images in both visible and infrared spectra. YOLO consistently demonstrates F1 scores below 26% in both spectra, while VJ’s scores lie in the interval from 38% to 61%. This performance discrepancy is discussed in view of paradigms’ strengths and weaknesses, whereas the relatively high scores of at least one detector are taken as empirical evidence in favor of the proposed data augmentation approach.

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Section: Viola-jones (Vj)mentioning

confidence: 99%

Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection

Santos,

Montalvão,

Araujo

et al. 2024

Sensors

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show abstract

“…Visual target detection has undergone two stages of development: traditional and deep learning-based target detection algorithms, which remarkably improved the detection accuracy and speed of image targets [18,19]. However, pure vision-based target detection still presents inherent disadvantages in addressing complex scenarios such as multiple target overlap, pedestrian detection in dense traffic, and fog.…”

Section: Introductionmentioning

confidence: 99%

Millimeter-Wave Radar and Vision Fusion Target Detection Algorithm Based on an Extended Network

Song

Zhang

et al. 2022

Machines

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The need for a vehicle to perceive information about the external environmental as an independent intelligent individual has grown with the progress of intelligent driving from primary driver assistance to high-level autonomous driving. The ability of a common independent sensing unit to sense the external environment is limited by the sensor’s own characteristics and algorithm level. Hence, a common independent sensing unit fails to obtain comprehensive sensing information independently under conditions such as rain, fog, and night. Accordingly, an extended network-based fusion target detection algorithm for millimeter-wave radar and vision fusion is proposed in this work by combining the complementary perceptual performance of in-vehicle sensing elements, cost effectiveness, and maturity of independent detection technologies. Feature-level fusion is first used in this work according to the analysis of technical routes of the millimeter-wave radar and vision fusion. Training and test evaluation of the algorithm are carried out on the nuScenes dataset and test data from a homemade data acquisition platform. An extended investigation on the RetinaNet one-stage target detection algorithm based on the VGG-16+FPN backbone detection network is then conducted in this work to introduce millimeter-wave radar images as auxiliary information for visual image target detection. We use two-channel radar and three-channel visual images as inputs of the fusion network. We also propose an extended VGG-16 network applicable to millimeter-wave radar and visual fusion and an extended feature pyramid network. Test results showed that the mAP of the proposed network improves by 2.9% and the small target accuracy is enhanced by 18.73% compared with those of the reference network for pure visual image target detection. This finding verified the detection capability and algorithmic feasibility of the proposed extended fusion target detection network for visually insensitive targets.

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“…Isaacs achieved object detection using an in situ weighted highlight-shadow detector, and performed a recognition process using an Ada-boosted decision tree classifier for underwater unexploded ordnance detection on simulated real aperture sonar data [26]. Feature selection is one of the most important steps in these machine learning algorithms, and Haar-like and Local Binary Patterns (LBP) are classical and common features in target detection [27][28][29][30][31]. On the whole, there are few studies on target detection in MWC images using features and classifiers.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Segmentation on Gas Plumes from Multibeam Water Column Images

et al. 2020

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The detection of gas plumes from multibeam water column (MWC) data is the most direct way to discover gas hydrate reservoirs, but current methods often have low reliability, leading to inefficient detections. Therefore, this paper proposes an automatic method for gas plume detection and segmentation by analyzing the characteristics of gas plumes in MWC images. This method is based on the AdaBoost cascade classifier, combining the Haar-like feature and Local Binary Patterns (LBP) feature. After obtaining the detected result from the above algorithm, a target localization algorithm, based on a histogram similarity calculation, is given to exactly localize the detected target boxes, by considering the differences in gas plume and background noise in the backscatter strength. On this basis, a real-time segmentation method is put forward to get the size of the detected gas plumes, by integration of the image intersection and subtraction operation. Through the shallow-water and deep-water experiment verification, the detection accuracy of this method reaches 95.8%, the precision reaches 99.35% and the recall rate reaches 82.7%. Integrated with principles and experiments, the performance of the proposed method is analyzed and discussed, and finally some conclusions are drawn.

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An Extended Set of Haar-like Features for Bird Detection Based on AdaBoost

Cited by 8 publications

References 7 publications

Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection

Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection

Millimeter-Wave Radar and Vision Fusion Target Detection Algorithm Based on an Extended Network

Automatic Detection and Segmentation on Gas Plumes from Multibeam Water Column Images

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