LANDMARK DETECTION USING DEEP LEARNING

In many computer vision applications, finding landmarks is a crucial challenge. In this thesis, we suggest a unique convolutional neural network-based method for landmark detection (CNNs). On the PASCAL VOC and COCO datasets, our solution surpasses cutting-edge techniques. The landmark detection method we suggest using CNN is built on a three-layer deep network. The input image’s features are extracted by the feature extraction layer, which is the first layer. The input image is categorised into one of the pre-established landmark categories in the second layer, which is a classification layer. A localization layer, which is the third layer, locates the landmarks in the input picture.