{"product_id":"kit-coral-usb-accelerator-object-detection","title":"Coral USB Accelerator Object Detection Kit with Raspberry Pi 4 + Camera","description":"\u003ch1\u003eRaspberry Pi 4 Coral USB Object Detection Kit — Build a Real-Time Edge AI Benchmarking Lab\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Intermediate\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 4-5 hours\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 16-21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Edge AI latency benchmarking\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eThis kit transforms a Raspberry Pi 4 and Google Coral USB accelerator into a real-time object detection system that identifies 90 everyday objects from a live camera feed. Powered by the SSD MobileNet model optimized for the Coral Edge TPU, the system achieves near real-time inference speeds. You will measure every millisecond of latency, building a dedicated benchmarking lab to understand the full pipeline from image capture to classification output.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou will construct a self-contained computer vision station that streams camera frames, runs object detection locally, and overlays bounding boxes with class labels and confidence scores. A logging interface captures inference latency per frame, enabling you to analyse performance bottlenecks. The final setup becomes a repeatable testbed for comparing model variants, frame rates, and precision trade-offs on embedded hardware.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eDeploy a TensorFlow Lite SSD MobileNet model on a Google Coral Edge TPU\u003c\/li\u003e\n  \u003cli\u003eIntegrate the Coral USB Accelerator with Raspberry Pi 4 and Pi Camera Module 2\u003c\/li\u003e\n  \u003cli\u003eMeasure and benchmark inference latency metrics (milliseconds per frame, throughput) with custom Python scripts\u003c\/li\u003e\n  \u003cli\u003eTune pipeline parameters to balance detection accuracy and frames per second for real-world edge applications\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 4 Model B 4GB\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCoral USB Accelerator\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi Camera Module 2\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMicroSD Card 32GB\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eCBSE Class 11–12 students pursuing the AI elective will find a tangible way to explore neural network inference. B.Tech ECE, CSE, and EEE undergraduates can use this for mini-projects, final‑year work, or Smart India Hackathon entries centred on real‑time computer vision. Makers at ATL Tinkering Labs and engineering colleges like IIT, NIT, VIT, and BITS will appreciate the repeatable benchmarking framework.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eOur AI companion walks you through debugging in real time, and you can reach the WhatsApp support team Monday to Saturday for personalised guidance on any roadblock.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I use this kit for a college major project submission?\u003c\/summary\u003e\u003cp\u003eYes. The build guide includes a full report template covering model selection, deployment, and latency analysis, making it directly usable for final-year B.Tech projects in ECE, CSE, or EEE.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes this kit work with a different camera module?\u003c\/summary\u003e\u003cp\u003eThe kit is pre-tested with the Pi Camera Module 2 for consistent latency benchmarks. Other USB cameras may function but require additional configuration and may alter performance results.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow does the latency benchmarking work?\u003c\/summary\u003e\u003cp\u003eYou'll run a provided Python script that captures frames, performs Coral‑accelerated inference, and logs the time for each detection. The toolkit includes graphing utilities to visualise latency trends across different resolutions and model settings.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eSSD MobileNet on Google Coral USB detects 90 object classes at near real-time speeds on Pi 4 — latency benchmarking lab.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/industrial-ph-sensor-module-for-arduino-esp32-raspberry-pi\"\u003eRaspberry Pi 4 Model B 4GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eCoral USB Accelerator\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/4-channel-relay-board-for-esp32-30-pin-5v-control\"\u003ePi Camera Module 2\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/microsd-card-reader-spi-module-for-arduino\"\u003eMicroSD Card 32GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Coral USB Accelerator Object Detection Kit with Raspberry Pi 4 + Camera?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Coral USB Accelerator Object Detection Kit with Raspberry Pi 4 + Camera includes all components needed: Raspberry Pi 4 Model B 4GB, Coral USB Accelerator, Pi Camera Module 2, MicroSD Card 32GB, USB-C PSU and more. 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