Effective Target Extraction of Automatic Target-Scoring System

Su, Zhongwei; Chen, Wei

doi:10.1109/iaeac47372.2019.8997574

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…To handle illumination variations, they used dynamic thresholding. Su and Chen [12] presented an approach for extracting targets to automate the scoring system. They applied a perspective transformation to convert input into a standard rectangle.…”

Section: Related Workmentioning

confidence: 99%

Application of YOLOv8 and Detectron2 for Bullet Hole Detection and Score Calculation from Shooting Cards

Butt,

Glas,

Monsuur

et al. 2023

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Scoring targets in shooting sports is a crucial and time-consuming task that relies on manually counting bullet holes. This paper introduces an automatic score detection model using object detection techniques. The study contributes to the field of computer vision by comparing the performance of seven models (belonging to two different architectural setups) and by making the dataset publicly available. Another value-added aspect is the inclusion of three variants of the object detection model, YOLOv8, recently released in 2023 (at the time of writing). Five of the used models are single-shot detectors, while two belong to the two-shot detectors category. The dataset was manually captured from the shooting range and expanded by generating more versatile data using Python code. Before the dataset was trained to develop models, it was resized (640 × 640) and augmented using Roboflow API. The trained models were then assessed on the test dataset, and their performance was compared using matrices like mAP50, mAP50-90, precision, and recall. The results showed that YOLOv8 models can detect multiple objects with good confidence scores. Among these models, YOLOv8m performed the best, with the highest mAP50 value of 96.7%, followed by the performance of YOLOv8s with the mAP50 value of 96.5%. It is suggested that if the system is to be implemented in a real-time environment, YOLOv8s is a better choice since it took significantly less inference time (2.3 ms) than YOLOv8m (5.7 ms) and yet generated a competitive mAP50 of 96.5%.

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Section: Related Workmentioning

confidence: 99%

Application of YOLOv8 and Detectron2 for Bullet Hole Detection and Score Calculation from Shooting Cards

Butt,

Glas,

Monsuur

et al. 2023

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“…The automatic dart-scoring system is a challenge work. Recently, the automatic target-scoring systems are presented with camera imaging methods [23][24][25][26][27][28]. The automatic bullet scoring system is presented for military training [23], which employs geometric rectification and the median filter, image binary, image subtraction between inter-frames to find the position of bullet-spot.…”

Section: Introductionmentioning

confidence: 99%

“…Automatic display system of the archery score is presented in [24], which used boundary line detection and arrow detection to determine the score. Effective target area [25] is detected using edge extraction and background extraction, and then perspective transformation. The scoring target for mobile shooting range detection employs external vibration sensing, and the morphological operation detects the effective outermost ring, finally SVM is used to find discriminating bullet hole candidates [26].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Blowgun Game Scoring Recognition System Based on Computer Vision

et al. 2021

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In this manuscript, we present score recognition techniques for blowgun game based on computer vision. First, the algorithm detects the position of the target, and calibrates the camera's parameters. Then the score is calculated with the detection of the dart tip on the target for real-time applications. To improve the robustness, the initial calibration is proposed to record N points as references at the edges of circles, to correct the camera position and angle. This approach can overcome the problems of lighting changes and the camera viewing angle deviation. The fast segmentation and orientation decision is proposed to find the blowgun tip accurately. The two cameras are presented to solve the overlapping problem of multiple darts to improve the detection accuracy. Based on calibration parameters, the distance weighting method is proposed to calculate the score precisely. Experiments result that the accuracy can achieve about 97% to recognize the score, and the processing speed can meet the real-time requirement with software implementation.

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Effective Target Extraction of Automatic Target-Scoring System

Cited by 2 publications

References 3 publications

Application of YOLOv8 and Detectron2 for Bullet Hole Detection and Score Calculation from Shooting Cards

Application of YOLOv8 and Detectron2 for Bullet Hole Detection and Score Calculation from Shooting Cards

Intelligent Blowgun Game Scoring Recognition System Based on Computer Vision

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