Fusing Multi-Level CNN Features to Detect RGB-T Salient Objects

PROJECT TITLE :

RGB-T Salient Object Detection via Fusing Multi-Level CNN Features

ABSTRACT:

Deep convolutional neural networks have lately made significant progress in the field of RGB-inducing salient object recognition (CNNs). The problem is that these detections have to contend with a wide range of tough situations that include low-light circumstances, varying light levels, and crowded backdrops. Instead of trying to improve RGB-based saliency detection, this research takes advantage of the complementing advantages of RGB and thermal infrared images. A novel end-to-end network for multi-modal salient object recognition is proposed, which turns the challenge of RGB-T saliency detection into a CNN feature-fusion issue. An initial backbone network is used to extract coarse features from each RGB or thermal infrared image, and then several ADFC modules are designed to extract multi-level refined features for each single-modal input image, taking into account that features captured at different depths differ in semantic information and visual details. Additionally, the cross-modal features from a pair of RGB-T images are combined using an MGF module, which utilises the ADFC modules for each level. As a final step, a joint attention guided bi-directional message passing (JABMP) module integrates the multi-level fused features from MGF modules to forecast saliency. Public RGB-T salient object datasets show that our proposed algorithm outperforms current state-of-the art algorithms in tough settings such as low contrast lighting and complex background conditions.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here