Object Detection for Night Surveillance Using Ssan Dataset Based Modified Yolo Algorithm in Wireless Communication

Murugan, R.; Sathyabama, B.

doi:10.1007/s11277-022-10020-9

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…In the backbone structure, it uses a feature pyramid structure for feature extraction. This structure can effectively fuse shallow features and deep features and detect targets of different sizes [18,19]. In addition, YOLOv5 also uses lightweight convolutional neural networks as backbones, such as MobileNetV3, to further reduce network complexity and improve the running speed.…”

Section: Basketball Object Detection Methods Using Deep Learningmentioning

confidence: 99%

Basketball Fixed-point Shot Hit Prediction Based on Human Pose Estimation Algorithm

Li,

Hua

2024

IJCAI

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As computer vision and artificial intelligence develop, the research on basketball fixed-point shooting hit prediction based on human pose estimation algorithm becomes a topic of great concern. To construct a basketball fixed-point shooting hit prediction model, a new object detection algorithm was designed, and the You Only Look Once version 5 algorithm was optimized on the basis of GIoU loss function and Convolutional Block Attention Module. Then, a new human pose estimation algorithmwas designed based on the OpenPose algorithm. Results showed that the average accuracy of the improved YOLOv5 algorithm reached 95.34% when the number of iterations was 50. In the comparison among improved OpenPose and other algorithms, improved OpenPose performed better, with a recall rate of 96.23%, an accuracy of 87.16%, a precision of 89.75%, and an F1 value of 88.19%. In the comparison with other models, the area of receiver operating characteristic curve was the largest, reaching 0.974, and the F1 value, accuracy and recall rate of the research model were the highest, reaching 95.54%, 96.39% and 98.25%, respectively. Results show that it effectively predicts the shoot hit of basketball fixed-point shooting, which provides a useful reference for tactical analysis and player performance evaluation in basketball games.Povzetek: Raziskava predstavlja napovedni model za zadetke pri košarkarskem metu s fiksne točke na osnovi algoritma za ocenjevanje človeške drže, kar je koristno za taktično analizo in ocenjevanje igralcev v košarki.

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Section: Basketball Object Detection Methods Using Deep Learningmentioning

confidence: 99%

Basketball Fixed-point Shot Hit Prediction Based on Human Pose Estimation Algorithm

Li,

Hua

2024

IJCAI

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“…Besides, night vision significantly impacts our daily visual efficiency and is crucial for enhancing safety and security. The research paper [66] places a primary focus on improving the night vision system to address societal concerns. Despite the importance of research on night vision, there is a notable gap in available databases for conducting studies utilizing deep-learning techniques.…”

Section: Related Workmentioning

confidence: 99%

An Improved YOLOv8 to Detect Moving Objects

Safaldin,

Zaghden,

Mejdoub

2024

IEEE Access

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Deep learning has revolutionized object detection, with YOLO (You Only Look Once) leading in real-time accuracy. However, detecting moving objects in visual streams presents distinct challenges. This paper proposes a refined YOLOv8 object detection model, emphasizing motion-specific detections in varied visual contexts. Through tailored preprocessing and architectural adjustments, we heighten the model's sensitivity to object movements. Rigorous testing against KITTI, LASIESTA, PESMOD, and MOCS benchmark datasets revealed that the modified YOLOv8 outperforms the state-of-the-art detection models, especially in environments with significant movement. Specifically, our model achieved an accuracy of 90%, a mean Average Precision (mAP) of 90%, and maintained a processing speed of 30 frames per second (FPS), with an Intersection over Union (IoU) score of 80%. This paper offers a detailed insight into object trajectories, proving invaluable in areas like security, traffic management, and film analysis where motion understanding is critical. As the importance of dynamic scene interpretation grows in artificial intelligence and computer vision, the proposed enhanced YOLOv8 detection model highlights the potential of specialized object detection and underscores the significance of our findings in the evolving field of object detection.

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“…Murugan.et al [9] presented Object Detection Based Modified Yolo Algorithm in Wireless Communication where the author used low-pass filter and unsharp filter to reduce noise and improve the sharpness of the image. A fast and effective lowillumination video enhancement algorithm was proposed by Hu.…”

Section: ░ 2 Literature Reviewmentioning

confidence: 99%

Adapting to the Dark: A Novel Adaptive Low Light Illumination Correction Algorithm for Video Sequences in Wireless Communications

Kiran Kumar,

Sethi,

Rawal

2023

IJEER

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The lower image quality and higher noise levels of low light images can have a substantial effect on wireless communication. Images typically have less brightness, less clarity, and more picture noise in low light situations. The precision and dependability of picture-based communication may be impacted by this decline in image quality. Low light levels can be detrimental to video chats since it becomes difficult for the camera to get clear pictures of the user's face. This may lead to decreased video quality, trouble with facial recognition, and a generally worse user experience when communicating over video. Low light illumination correction algorithms are frequently made to work quickly and effectively in order to enable real-time video-based wireless communication. Correcting uneven illumination or low light in images is crucial for various fields; as such images pose challenges for human recognition, computer vision algorithms, and multimedia algorithms. An adaptive illumination correction algorithm based on a simple log transformation approach enhanced by a hyperbolic beta transformation and a logarithmic image processing is presented in this research. It has an adaptive optimization function that adjusts itself based on the image’s illumination and reflectance. Low computation and minimal parameter optimization are the hallmarks of this method. A variety of illuminated images and video sequences from various datasets are used to evaluate the suggested adaptive illumination approach. This adaptive illumination approach's experimental findings are impressive compared to those produced using prior techniques. The results reveal that the proposed system can adapt to various image-based communication and surveillance video sequences taken under various illumination conditions.

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Object Detection for Night Surveillance Using Ssan Dataset Based Modified Yolo Algorithm in Wireless Communication

Cited by 7 publications

References 20 publications

Basketball Fixed-point Shot Hit Prediction Based on Human Pose Estimation Algorithm

Basketball Fixed-point Shot Hit Prediction Based on Human Pose Estimation Algorithm

An Improved YOLOv8 to Detect Moving Objects

Adapting to the Dark: A Novel Adaptive Low Light Illumination Correction Algorithm for Video Sequences in Wireless Communications

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