Workflow of the DeepDetect Framework

Published 2026-02-10 in Deep Learning

The figure illustrates the three main stages involved in the development of the DeepDetect framework , designed for robust and dense keypoint detection in images. The entire pipeline transforms raw images into semantically meaningful keypoints using a combination of classical computer vision techniques and deep learning. (a) Data Preparation: Image to Fusion Masks In the first stage, raw input images are processed to generate fusion masks . Instead of relying on a single feature extractor, DeepDetect combines the strengths of multiple traditional computer vision methods: Keypoint detectors: SIFT, ORB, BRISK, FAST, AGAST, Harris, and Shi–Tomasi Edge detectors: Canny and Sobel Each detector captures different structural and geometric properties of the image. The outputs from these detectors are fused together to create binary masks , which provide rich and diverse visual representations. These fusion masks serve as automatic labels , eliminating the need for manual annotation and improving robustness across different image conditions. (b) Training Stage: Learning from Fusion Masks In the second stage, the generated fusion masks are used as supervisory signals to train a deep neural…

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