The content discusses the improvements made to the feature extraction network, detection head, and regression loss in the VarifocalNet model. The feature extraction network uses a new bottleneck module to capture smaller object features effectively. The improved detection head introduces a feature interactor that enhances category-related features through dynamic convolution. The regression loss is enhanced by considering center point deviations between predicted and ground truth boxes, resulting in a more effective loss function. The overall model structure is depicted, highlighting the changes made to improve detection accuracy and feature utilization. The improved GIoU loss is detailed, showing how it measures the deviation between predicted and ground truth boxes more accurately. The complete loss function combines various components to optimize detection performance. These enhancements aim to improve the accuracy and efficiency of object detection in the VarifocalNet model.
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Source link: https://www.nature.com/articles/s41598-024-66234-3
Improved VarifocalNet for detecting defects in photovoltaic modules #SolarDefectDetection
Preeti
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