Implementation of Single Shot Multibox Detector (SSD) Algorithm for Object Detection

This study presents an innovative implementation of the Single Shot Multibox Detector (SSD) algorithm for real-time object detection on the Raspberry Pi 4. It harnesses the synergies of Python programming, the OpenCV computer vision library, and the SSD algorithm's adaptability. Python, chosen for its versatility, is the programming language, while OpenCV forms the backbone for implementing the finely tuned SSD algorithm. The resulting modular script, designed for real-time object detection, incorporates optimization techniques such as frame skipping and region of interest cropping to enhance Raspberry Pi 4 performance. The system demonstrates promising results, showcasing accurate and responsive object detection in resource-constrained environments. The Raspberry Pi 4's versatility makes it the solution for diverse edge computing applications, including surveillance, robotics, and intelligent environments. Computational constraints prompted the adoption of optimizations, while identified challenges illuminate future research directions exploring advanced algorithms and considering more potent hardware variants. This study contributes significantly to the intersection of computer vision and edge computing, underscoring the successful implementation of the SSD algorithm on the Raspberry Pi 4 and encapsulating technical achievements, practical implications, and a roadmap for future advancements in real-time object detection.