🚀 Let's build a computer vision pipeline for a real-world robotics project 🚀
(Top 10 computer vision projects {with links to resources} for beginners: https://lnkd.in/evPTbuDf)
🔧 Industrial Case Study: Automating Warehouse Sorting of Electronic Gadget Boxes using segmentation-based grasping 🔧
🔍 Problem Statement: 🔍
🧩 Scenario: A robotic arm needs to autonomously grasp and sort flat rectangular boxes of electronic gadgets in a warehouse. 🧩
🏢 Context: An e-commerce company needs to sort red and blue variants of a popular electronic gadget into separate bins. 🏢
🎯 Objective: Develop a computer vision system to identify and segment boxes to find the grasp center for the suction gripper, enabling the robotic arm to sort the objects based on color. 🎯
🔍 Step 1: Define the Problem Statement 🔍
📋 Requirements: The system must accurately identify and sort flat rectangular boxes based on color in real-time. 📋
📏 Constraints: Efficient handling of similar-looking objects with different colors, reliable performance in varying lighting conditions, and real-time processing. 📏
🔍 Step 2: Background Research 🔍
📚 Explore Various Approaches such as Conventional Computer vision-based (Uses edge detection, color filtering, and contour analysis), SegmentAnything, MaskR-CNN, FastSAM, etc. and, analyse and select an approach based on your constraints and requirements
⚡ Fast Segment Anything (FastSAM):
🌟 Optimized for speed and accuracy. 🌟
📈 Pros: Balances speed and precision, ideal for real-time applications. 📈
🔍 Step 3: Data Collection and Annotation 🔍
📸 Gather Images:
📷 Capture images of red and blue gadget boxes from multiple angles on the conveyor belt. 📷
💾 Organize images into categories based on color and variant. 💾
🖍️ Annotate Images:
🏷️ Use tools like LabelImg to create bounding boxes and segmentation masks, focusing on grasp centers. 🏷️
📑 Label datasets with metadata such as object type, color, and grasp center. 📑
🔍 Step 4: Model Selection and Training 🔍
🤖 Choose FastSAM for Segmentation for better real-time segmentation:
🏋️ Train the Model:
🔧 GitHub Repository: FastSAM Implementation 🔧
📊 Training: Use annotated images, fine-tune hyperparameters for optimal performance. 📊
🔍 Step 5: Model Evaluation and Optimization 🔍
🧪 Evaluate Model Performance:
📊 Validate with a separate dataset to measure accuracy and precision. 📊
⚙️ Optimize Model:
🔍 Fine-tune hyperparameters and perform data augmentation. 🔍
🔄 Experiment with different architectures for better performance. 🔄
🔍 Step 6: Integration with Robotic System 🔍
🛠️ Develop Integration Pipeline:
🖥️ Connect the segmentation model to the robotic arm's control system using ROS (Robot Operating System). 🖥️
🔄 Ensure real-time processing and feedback loop for precise grasping. 🔄
NOTE: Image is generated using DALL-E