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Cover image for Raspberry Pi CM5 IO Board
Raspberry Pi CM5 IO Board with USB 2.0 OTG The official Raspberry Pi Compute Module 5 IO board doesn’t include the USB 2.0 path switch IC, which was in its elder brother CM4 IO board. Also, it uses CM5’s own CC control pins for USB Type C port. To make my design compatible with both CM4 and CM5, I integrated an USB 2.0 switch IC in the similar way, but with a little bit different configuration. The board need to have 2 x USB 2.0 ports. So I needed to add an USB 2.0 hub IC and an USB path switch chip, similar to the official Raspberry Pi CM4 IO board. But I needed to modify it to use an USB Type-C port and use CC control pins of CM5. The elder CM4 series modules didn’t have CC control pins. Thus, I also added DNP resistors, where I can solder 5.1K resistors for using CM4 on this board. The official Raspberry CM4 IO board used VBUS to automatically switch the OTG config via USB_OTG_ID pin. But on my design, +5V line may be supplied from external power source. It means I should use 5A rated ideal diode. But the requirements was only using USB 2.0 as normal Host, instead of standard USB OTG compatible port. As we anyway need BOOT button to set the compute module into flashing mode, I decided to use a DPDT slide switch instead of tactile boot button. In this way, I could manage setting BOOT and OTG_ID flags using a single switch sliding.
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Cover image for Automated Video Dehazing & Atmospheric
Automated Video Dehazing & Atmospheric Haze Simulation System 🚀 Project Overview This advanced Computer Vision project is designed to address visibility challenges in adverse weather conditions. The system features a dual-module architecture: it can synthetically inject realistic atmospheric fog/haze into crystal-clear video streams for dataset generation, and conversely, restore heavily degraded, foggy videos into crisp, high-visibility outputs in real-time. 🛠️ Core Functionality & Modules Module 1: Atmospheric Haze Simulation Purpose: Generates synthetic datasets to train and benchmark object detection models (like YOLO) for bad weather conditions. How it works: Implements mathematical scattering models to calculate depth maps and overlay a realistic layer of dense fog or smoke over clean video frames. Module 2: Real-Time Video Dehazing Purpose: Restores clarity and vivid color to video streams captured in low-visibility environments. How it works: Leverages physics-based Computer Vision algorithms (such as Dark Channel Prior - DCP) or Deep Learning frameworks to estimate atmospheric light, eliminate transmission noise, and reconstruct the scene's original contrast. 🎯 Use Cases & Applications Autonomous Vehicles: Enhances the sight and reliability of self-driving car sensors in dense fog. Smart Surveillance (CCTV): Improves security monitoring and facial recognition accuracy under harsh outdoor weather. Drone Navigation: Aids aerial drones in safely navigating through smoke, dust storms, or low-lying clouds. 💻 Tech Stack Used Language: Python Libraries: OpenCV, NumPy, Matplotlib, PyTorch / TensorFlow (if deep learning was applied) Concepts: Image Processing, Atmospheric Scattering Models, Feature Restoration, Video Pipeline Optimization
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