SAR ship detection based on YOLOv5

Tian, Ming; Ju, Yanwei

doi:10.1117/12.2660100

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The YOLOv5 model begins by adaptively scaling the input image and utilizing a genetic algorithm to automatically learn and adjust the depth and width of the network to meet the needs of different scenes [49]. It consists of four modules: input, backbone, neck, and prediction, which work together to achieve its objective [50]. YOLOv5 uses CSPNet as the backbone to extract features, achieving a better balance of inference speed and accuracy.…”

Section: Object Detectionmentioning

confidence: 99%

Analyzing computer vision models for detecting customers: a practical experience in a mexican retail

Fernández Del Carpio

2024

Int. J. Adv. Intell. Informatics

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Computer vision has become an important technology for obtaining meaningful data from visual content and providing valuable information for enhancing security controls, marketing, and logistic strategies in diverse industrial and business sectors. The retail sector constitutes an important part of the worldwide economy. Analyzing customer data and shopping behaviors has become essential to deliver the right products to customers, maximize profits, and increase competitiveness. In-person shopping is still a predominant form of retail despite the appearance of online retail outlets. As such, in-person retail is adopting computer vision models to monitor store products and customers. This research paper presents the development of a computer vision solution by Lytica Company to detect customers in Steren’s physical retail stores in Mexico. Current computer vision models such as SSD Mobilenet V2, YOLO-FastestV2, YOLOv5, and YOLOXn were analyzed to find the most accurate system according to the conditions and characteristics of the available devices. Some of the challenges addressed during the analysis of videos were obstruction and proximity of the customers, lighting conditions, position and distance of the camera concerning the customer when entering the store, image quality, and scalability of the process. Models were evaluated with the F1-score metric: 0.64 with YOLO FastestV2, 0.74 with SSD Mobilenetv2, 0.86 with YOLOv5n, 0.86 with YOLOv5xs, and 0.74 with YOLOXn. Although YOLOv5 achieved the best performance, YOLOXn presented the best balance between performance and FPS (frames per second) rate, considering the limited hardware and computing power conditions.

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Section: Object Detectionmentioning

confidence: 99%

Analyzing computer vision models for detecting customers: a practical experience in a mexican retail

Fernández Del Carpio

2024

Int. J. Adv. Intell. Informatics

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“…Consequently, the state-of-the-art YOLO models closely rival their two-stage competitors in accurately classifying and locating targets while preserving its speed advantage. Moreover, the performance of YOLOv5 can be enhanced by incorporating (BiFPN) to augment feature fusion and refine the model’s capabilities in a recent study [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

The investigation of constraints in implementing robust AI colorectal polyp detection for sustainable healthcare system

2023

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Colorectal cancer (CRC) is one of the significant threats to public health and the sustainable healthcare system during urbanization. As the primary method of screening, colonoscopy can effectively detect polyps before they evolve into cancerous growths. However, the current visual inspection by endoscopists is insufficient in providing consistently reliable polyp detection for colonoscopy videos and images in CRC screening. Artificial Intelligent (AI) based object detection is considered as a potent solution to overcome visual inspection limitations and mitigate human errors in colonoscopy. This study implemented a YOLOv5 object detection model to investigate the performance of mainstream one-stage approaches in colorectal polyp detection. Meanwhile, a variety of training datasets and model structure configurations are employed to identify the determinative factors in practical applications. The designed experiments show that the model yields acceptable results assisted by transfer learning, and highlight that the primary constraint in implementing deep learning polyp detection comes from the scarcity of training data. The model performance was improved by 15.6% in terms of average precision (AP) when the original training dataset was expanded. Furthermore, the experimental results were analysed from a clinical perspective to identify potential causes of false positives. Besides, the quality management framework is proposed for future dataset preparation and model development in AI-driven polyp detection tasks for smart healthcare solutions.

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SAR ship detection based on YOLOv5

Cited by 2 publications

References 11 publications

Analyzing computer vision models for detecting customers: a practical experience in a mexican retail

Analyzing computer vision models for detecting customers: a practical experience in a mexican retail

The investigation of constraints in implementing robust AI colorectal polyp detection for sustainable healthcare system

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