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Concealed object detection through customized YOLO model
(Department of Computer Science & Engineering, NITS, 2022) Annadurai, Karthic
Concealed Object detection (COD) is a primary need for the automatic security surveil lance system. To detect those objects, first, we need to acquire high-quality data for training the model. Here Terahertz image is being used, which is captured in the fre quency range of 140 GHz that will not cause any harm to humans. Even pregnant women and heart patients can walk through the Terahertz image sensing equipment without any hesitation. Accuracy of the COD is analyzed by training various YOLO models with the customized parameters and the Terahertz Imaging dataset. Various methodologies and analyses have been proposed in this project. The analysis report will be useful for further research activities. YOLO (You Only Look Once) version 5 uses a non-max suppression algorithm by default to calculate the bounding box of the concealed objects. To enhance the accuracy, an ensemble technique available called Weighted Boxes Fusion (WBF) can be implemented. To overcome the data overfitting or less amount of dataset, the augmentation techniques like any adversarial generative neural network can be applied. Labeling those augmented images are mandatory for training any YOLO models.