Cascade and Fusion: A Deep Learning Approach for Camouflaged Object Sensing.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Sensor Technology

Background:

• Traditional object detection struggles with low signal-to-noise ratio features common in camouflaged objects.
• Applications in biological research, remote sensing, and military contexts necessitate improved camouflage detection.

Purpose of the Study:

• To develop a deep learning framework, CODCEF, for effective sensor-based detection of camouflaged objects.
• To overcome limitations of traditional algorithms in extracting features from noisy, low-SNR data.

Main Methods:

• Proposed Camouflaged Object Detection with Cascade and Feedback Fusion (CODCEF) framework using an RGB optical sensor.
• Implemented a cascaded structure with Feedback Partial Decoders (FPD) instead of a standard encoder-decoder.
• Introduced Pixel Perception Fusion (PPF) loss to focus on object edge pixels.

Main Results:

• CODCEF demonstrated reduced information loss and noise interference during feature interweaving via FPD.
• The PPF loss improved attention to critical edge pixels for object identification.
• Experimental results on an edge device showed CODCEF achieved competitive performance against 10 state-of-the-art methods.

Conclusions:

• CODCEF offers a robust deep learning solution for camouflaged object detection.
• The proposed architecture and loss function effectively handle low-SNR features and enhance edge detection.
• CODCEF presents a significant advancement for applications requiring precise detection of hidden objects.