Detecting Soccer Balls with Reduced Neural Networks

Meneghetti, Douglas De Rizzo; Homem, Thiago Pedro Donadon; Oliveira, Jonas Henrique Renolfi de; Silva, Isaac Jesus da; Perico, Danilo H.; Bianchi, Reinaldo A. C.

doi:10.1007/s10846-021-01336-y

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…The presence of graphics processing units (GPU)-accelerated parallel processing units in mobile embedded systems provides more resilience for complex calculations, especially in real-time applications [4]. Owing to the rapid developments in detection and classification algorithms, it has become possible to detect and classify objects in real-time with limited computing power [5,6].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Indoor Path Planning Using Object Detection for Autonomous Flying Robots

Alparslan¹,

Çetin²

2023

Intelligent Automation &Amp; Soft Computing

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Unknown closed spaces are a big challenge for the navigation of robots since there are no global and pre-defined positioning options in the area. One of the simplest and most efficient algorithms, the artificial potential field algorithm (APF), may provide real-time navigation in those places but fall into local minimum in some cases. To overcome this problem and to present alternative escape routes for a robot, possible crossing points in buildings may be detected by using object detection and included in the path planning algorithm. This study utilized a proposed sensor fusion method and an improved object classification method for detecting windows, doors, and stairs in buildings and these objects were classified as valid or invalid for the path planning algorithm. The performance of the approach was evaluated in a simulated environment with a quadrotor that was equipped with camera and laser imaging detection and ranging (LIDAR) sensors to navigate through an unknown closed space and reach a desired goal point. Inclusion of crossing points allows the robot to escape from areas where it is congested. The navigation of the robot has been tested in different scenarios based on the proposed path planning algorithm and compared with other improved APF methods. The results showed that the improved APF methods and the methods reinforced with other path planning algorithms were similar in performance with the proposed method for the same goals in the same room. For the goals outside the current room, traditional APF methods were quite unsuccessful in reaching the goals. Even though improved methods were able to reach some outside targets, the proposed method gave approximately 17% better results than the most successful example in achieving targets outside the current room. The proposed method can also work in real-time to discover a building and navigate between rooms.

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Section: Introductionmentioning

confidence: 99%

Real-Time Indoor Path Planning Using Object Detection for Autonomous Flying Robots

Alparslan¹,

Çetin²

2023

Intelligent Automation &Amp; Soft Computing

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“…This model has a high detection speed and is capable of multi-object detection. A YOLOv4-Lite single-tree detection method is proposed to further integrate the YOLOv4 network model (Meneghetti et al, 2021), simplify the entire feature extraction network, optimize loss, and enhance sample data.…”

Section: Introductionmentioning

confidence: 99%

YOLOv4-Lite–Based Urban Plantation Tree Detection and Positioning With High-Resolution Remote Sensing Imagery

Zheng

2022

Front. Environ. Sci.

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Automatic tree identification and position using high-resolution remote sensing images are critical for ecological garden planning, management, and large-scale environmental quality detection. However, existing single-tree detection methods have a high rate of misdetection in forests not only due to the similarity of background and crown colors but also because light and shadow caused abnormal crown shapes, resulting in a high rate of misdetections and missed detection. This article uses urban plantations as the primary research sample. In conjunction with the most recent deep learning method for object detection, a single-tree detection method based on the lite fourth edition of you only look once (YOLOv4-Lite) was proposed. YOLOv4’s object detection framework has been simplified, and the MobileNetv3 convolutional neural network is used as the primary feature extractor to reduce the number of parameters. Data enhancement is performed for categories with fewer single-tree samples, and the loss function is optimized using focal loss. The YOLOv4-Lite method is used to detect single trees on campus, in an orchard, and an economic plantation. Not only is the YOLOv4-Lite method compared to traditional methods such as the local maximum value method and the watershed method, where it outperforms them by nearly 46.1%, but also to novel methods such as the Chan-Vese model and the template matching method, where it outperforms them by nearly 26.4%. The experimental results for single-tree detection demonstrate that the YOLOv4-Lite method improves accuracy and robustness by nearly 36.2%. Our work establishes a reference for the application of YOLOv4-Lite in additional agricultural and plantation products.

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“…(ii) it is important not only to get high‐quality images but also to have images with explicit ball appearance as much as possible (i.e., the ball must not be occluded behind players). Concerning the first issue, different soft computing techniques such as fuzzy 8 and neural networks 9 could be employed to promote the performance of the detection system. On the other hand, resolving the second issue requires interactive photographing 10 in which the camera is a movable one allowing continuous ball tracking.…”

Section: Introductionmentioning

confidence: 99%

A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

Abulwafa

Saleh

Saraya

et al. 2021

Int J of Intelligent Sys

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Sports video analysis has received much attention as it is turned to be a hot research area in the field of image processing. This led to opportunities to develop fascinating applications supported by analysis of different sports especially football. Identifying the ball in soccer images is an essential task for not only goal scoring but also players' evaluation. However, soccer ball detection suffers from several hurdles such as; occlusions, fast moving objects, shadows, poor lighting, color contrast, and other static background objects. Although several ball detection techniques have been introduced such as; Frame Difference, Mixture of Gaussian (MoG), Optical Flow and etc., ball detection in soccer games is still an open research area. In this paper, a new Fuzzy Based Ball Detection (FB 2 D) strategy is proposed for identifying the ball through a set of image sequences extracted form a soccer match video. FB 2 D has the ability to accurately identify the ball even if it is attached to the white lines drawn on the playground or partially occluded behind players. FB 2 D has been compared to recent ball detection techniques. Experimental results have shown that FB 2 D outperforms recent detection techniques as it introduced the maximum accuracy and the accuracy of detection in the testing stage is close to 100%. As well as the minimum error.

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Detecting Soccer Balls with Reduced Neural Networks

Cited by 6 publications

References 30 publications

Real-Time Indoor Path Planning Using Object Detection for Autonomous Flying Robots

Real-Time Indoor Path Planning Using Object Detection for Autonomous Flying Robots

YOLOv4-Lite–Based Urban Plantation Tree Detection and Positioning With High-Resolution Remote Sensing Imagery

A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

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