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[IEEE 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Shenzhen, China (2018.7.13-2018.7.15)] 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Spacecraft Detection Based on Deep Convolutional Neural Network
摘要： Spacecraft detection is one of essential issues on aerospace information processing and control, and can provide reliable dynamic state of target, so as to support decisions made on target recognition, classification, catalogue, et al. Although numerous spacecraft detection methods exist, most of them cannot achieve real-time detection, and are still lack of better accuracy and fault-tolerance for different scenes. Recently, deep learning algorithms have achieved fantastic detection performance in computer vision community, especially the regression-based convolutional neural network YOLOv2, which has good accuracy and speed, and outperforming other state-of-the-art detection methods. This paper for the first time applies CNN to the detection of spacecraft and sets up a dataset for target detection in space. Our method starts with image annotation and data augmentation, and then uses our improved regression-based convolutional neural network YOLOv2 to detect spacecraft in an image. The experimental results have shown that our algorithm achieves 97.8% detection rate in the test set, and the average detection time of each image is about 0.018s, which has lower time overhead and better robustness to rotation and illumination changes of spacecraft.
关键词： Spacecraft,CNN,Target detection,YOLOv2
更新于2025-09-23 15:23:52
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Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network
摘要： Background: In this study, images of 2450 benign thyroid nodules and 2557 malignant thyroid nodules were collected and labeled, and an automatic image recognition and diagnosis system was established by deep learning using the YOLOv2 neural network. The performance of the system in the diagnosis of thyroid nodules was evaluated, and the application value of artificial intelligence in clinical practice was investigated. Methods: The ultrasound images of 276 patients were retrospectively selected. The diagnoses of the radiologists were determined according to the Thyroid Imaging Reporting and Data System; the images were automatically recognized and diagnosed by the established artificial intelligence system. Pathological diagnosis was the gold standard for the final diagnosis. The performances of the established system and the radiologists in diagnosing the benign and malignant thyroid nodules were compared. Results: The artificial intelligence diagnosis system correctly identified the lesion area, with an area under the receiver operating characteristic (ROC) curve of 0.902, which is higher than that of the radiologists (0.859). This finding indicates a higher diagnostic accuracy (p = 0.0434). The sensitivity, positive predictive value, negative predictive value, and accuracy of the artificial intelligence diagnosis system for the diagnosis of malignant thyroid nodules were 90.5%, 95.22%, 80.99%, and 90.31%, respectively, and the performance did not significantly differ from that of the radiologists (p > 0.05). The artificial intelligence diagnosis system had a higher specificity (89.91% vs 77.98%, p = 0.026). Conclusions: Compared with the performance of experienced radiologists, the artificial intelligence system has comparable sensitivity and accuracy for the diagnosis of malignant thyroid nodules and better diagnostic ability for benign thyroid nodules. As an auxiliary tool, this artificial intelligence diagnosis system can provide radiologists with sufficient assistance in the diagnosis of benign and malignant thyroid nodules.
关键词： Thyroid nodules,Ultrasound,Artificial intelligence,Computer-aided diagnosis systems,YOLOv2 neural network
更新于2025-09-23 15:22:29