With the continuous progress of society, people's lifestyle and quality of life are constantly changing, although the overall life expectancy of human beings has been extended, but lung diseases are still a serious threat to human health. In order to improve the object detection performance of lung nodules, the Fast R-CNN model was improved. By adding the CBAM attention mechanism module to the convolutional layer of the ResNet-50 and VGG16 feature extraction models, the model focuses more on the key feature extraction in the image. Due to the small size of pulmonary nodules in CT images, a object size selection box based on statistical data was introduced, and the improved Fast R-CNN-VGG16- CBAM model was used in the experiment with VGG16 as the backbone. Compared with the original model, the precision is increased by 6.01 percentage points, the recall rate is increased by 3.34 percentage points, and the mAP value is increased by 4.54 percentage points, and the improved Fast R-CNN-Resnet50-CBAM model is improved by 2.71 percentage points, the recall rate is increased by 4.6 percentage points, and the mAP value is increased by 4.03 percentage points. In addition, compared with the first-stage detection model YOLOV3, the improved Fast R-CNN-Resnet50- CBAM model has 5.2 percentage points higher precision and 13.36 percentage points higher mAP value. Experimental results show that the designed Fast R-CNN-Resnet50-CBAM model can effectively improve the performance of lung nodule object detection.
Railroad transportation is an important infrastructure for public travel and cargo transportation. With the rapid development of railroad construction, the mileage of operation continues to increase and the road network continues to improve, and the railroad covers a wider range of terrain, making the environment for train travel more complex. The behavior of individuals or groups entering the railroad track without permission or authorization may cause serious harm to the life safety of personnel and the normal operation of railroad traffic. Therefore, this paper aimed to propose an improved YOLOV5-based track personnel intrusion detection algorithm, which improves the recall rate by 14% and reduces the loss rate by introducing the CBAM attention mechanism into the C3 layer of the three pyramid strata of YOLO, achieving an average precision of 98%. The results of experimental simulation using the improved model on the acquired image data to be detected for unauthorized personnel intrusion into the track show that the machine vision-based railroad track personnel intrusion detection algorithm in this paper takes full account of the characteristics of the railroad scenario, and the processing has a high detection precision. The finding of the study can make contribution to the Railway Bureau to effectively detect the risk of railroad safety and reduce the probability of accidents.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.