ATTENTION ARCHITECTURE
Channel + Pixel attention integration improving detection accuracy in complex rescue environments.
Research Dossier / 02
Human Body Parts Detection using Improved YOLOv5 with Multi-Layer Attention Network (MLA-NET)
Designed an enhanced YOLOv5-based detection framework integrated with a Multi-Layer Attention Network (MLA-NET) for accurate human body part detection in disaster rescue scenarios.
Short LabelMLA-NET YOLOv5
- Institution
- Research under Dr. Soumajit Pramanik, IIT Bhilai
- Role
- Computer Vision Researcher
- Active Period
- 2023 - Present
YOLOv5
PyTorch
OpenPose
Real-ESRGAN
Deep Learning
Method + Iterations
Step 01
Developed a multi-layer attention module combining channel attention and pixel attention to improve feature refinement.
Step 02
Automated ground-truth generation using OpenPose-based body joint estimation.
Step 03
Enhanced low-resolution disaster footage using Real-ESRGAN super-resolution models.
Step 04
Implemented frame extraction pipelines for video-to-image dataset creation.
Step 05
Built augmentation sets targeting smoke/noise/blur artifacts common in rescue operations.
Step 06
Achieved improved robustness in detecting partially occluded or irregularly posed victims.
Visual Notes
TRAINING PIPELINE
Dataset generation workflow from raw video streams to curated frame-level labels and model-ready samples.
INFERENCE UNDER STRESS
Validation runs across low-light, occluded, and compressed inputs to stress-test deployment resilience.