📦 Deep Learning Ensemble for Automated Pallet Counting

This repository provides a real-time solution for automated pallet counting in operational warehouses using deep learning ensemble models. Our system predicts the total, CHEP, and EPAL pallet counts from RGB images through a multi-target regression approach with explainable AI.

🔍 Overview

Manual pallet counting is still widely used despite being error-prone and labor-intensive. We propose an ensemble of CNN backbones to replace this with a vision-based system that is:

• Accurate: MAE as low as 1.15 pallets
• Explainable: Grad-CAM for model transparency

📊 Key Results

Model	MAE (Total)	MAE (CHEP)	MAE (EPAL)	R²
EfficientNet-B3	1.401	0.991	1.009	0.840
ResNet-50	1.918	1.682	1.475	0.729
ConvNeXt-Tiny	1.696	0.721	1.426	0.702
Ensemble	1.151	0.918	0.857	0.875

🧠 Model Architecture

• Backbones: EfficientNet-B3, ResNet-50, ConvNeXt-Tiny
• Task: Multi-target regression for [CHEP, EPAL] with total = CHEP + EPAL
• Loss: Smooth L1
• Explainability: Grad-CAM visualizations on final conv layers

🛠 Training & Augmentation

• Framework: PyTorch
• Image size: 224×224
• Augmentations: Crop, flip, affine, color jitter, blur, compression, dropout
• Optimizer: AdamW with One-Cycle LR

📁 Dataset

A proprietary dataset of 130 real-world warehouse images with manual annotations. Due to privacy concerns, the dataset is not publicly available. Contact for potential access or collaboration.

📂 Repository Structure

├── CNN-Pallet.ipynb
├── dataset/
│ ├── images/
│ └── labels.csv
└── README.md

📈 Explainable AI

Grad-CAM visualizations show attention regions for each model:

• EfficientNet focuses on stack edges and shadows
• ResNet highlights CHEP markings and textures
• ConvNeXt captures broader stack areas with robustness to lighting

💡 Future Work

• Expand dataset across multiple facilities
• Integrate RGB-D or stereo vision for depth-aware counting
• Explore temporal modeling with video streams