隨著物聯網與人工智慧及雲端運算的高速發展，傳統笨重的PC皮膚頭皮檢測儀器系統已經無法滿足消費者對於隨時、隨地獲取自身皮膚頭皮資訊的需求，因此結合移動裝置與具有人工智能的雲端皮膚頭皮檢測與分析系統，市場前景看好。另外近年來，深度學習已應用於臨床醫學影像分析中，有優異的表現。隨著這些方法的持續發展，具有深度學習功能的產品已廣泛應用在美容美髮與醫美大健康行業中。
本文以皮膚和頭皮檢測為研究標的,開發了一套以深度學習檢測算法的雲端皮膚和頭皮檢測系統，提供12項檢測項目，並對美髮和醫美需求的頭髮密度測量和頭髮髮徑測量，做了深度學習算法的研究和實現。本文的具體研究內容如下：
1.完成一個可以兩千台以上裝置同時運作，基於雲端的智能頭皮及皮膚檢測系統，系統已實際應用在2000家以上醫美和沙龍門店，並且建立了一個超過幾百萬張頭皮和皮膚數據資料庫。
2.進行以美髮沙龍比超商密集的場域， 藉由 AI檢測和體驗延伸消費商機。置入社群互動(Line,FB,Whatsapp)推送檢測報告、智慧推薦、分潤商城、支付等服務，讓消費者從服務體驗到產品導購。解決店家美容產品庫存和銷售的問題，讓傳統服務場域轉型為新時代的最佳零售場域！
3. 頭髮密度測量（HDM）和頭髮髮徑 ,常被用來判斷頭髮及頭皮健康程度。我們完成一個長短髮狀況都可以辦別，以偏光鏡頭取樣照片，可以避免頭皮和毛髮油垢產生影像干擾，可以直接標定毛囊和分辨1-3株毛髮建構一個髮密度檢測算法。我們以實驗證明修改yolov5-our演算法，比更先進的yolov7有更高的準確率(mAP@50 77.1 Vs 73.8) ,由Yolov5s改進增加了5.2%的準確率(mAP@50 71.9-->77.1)。

With the rapid development of the Internet of Things, artificial intelligence and cloud computing, the traditional bulky PC skin and scalp detection instrument system has been unable to meet the needs of consumers to obtain their own skin and scalp information anytime and anywhere. Therefore, the skin and scalp detection and analysis system which combines mobile devices and cloud-based artificial intelligence has a promising market prospect. In addition, in recent years, deep learning has been applied to clinical medical image analysis with excellent performance. With the continuous development of these methods, products with deep learning functions have been widely used in the beauty ,hairdressing ,aesthetic medicine and health industries.
Taking skin and scalp detection as the research object, this paper develops a cloud-based skin and scalp detection system based on deep learning detection algorithms, provides 12 detection items, and measures hair density and diameter for hairdressing and aesthetic medicine needs. Research and implementation of deep learning algorithms. The specific research contents of this paper are as follows:
1. Complete a cloud-based intelligent scalp and skin detection system that can operate more than 2,000 devices at the same time. The system has been practically applied to more than 2,000 beauty shops and hair salons, and a database of more than several million scalp and skin data has been established.
2. Carry out areas where hair salons are denser than Convenience stores, and extend consumption opportunities through AI detection and experience. Incorporate community interaction (Line, FB, Whatsapp) send test report, intelligent recommendation, profit-sharing mall, payment ,and other services, allowing consumers to experience from service to product shopping guide. Solve the problem of inventory and sales of beauty products in stores, and transform traditional service areas into the best retail areas in the new era!
3. Hair Density Measurement (HDM) and hair diameter are often used to judge the health of hair and scalp. We can distinguish between long and short hair conditions. Using a polarized lens to sample photos can avoid image interference caused by scalp and hair grease, and can directly calibrate hair follicles and distinguish 1-3 hairs to construct a hair density detection algorithm. We experimentally prove that the modified yolov5-our algorithm has higher accuracy than the more advanced yolov7 (mAP@50 77.1 Vs 73.8), and the Yolov5s improvement increases the accuracy by 5.2% (mAP@50 71.9-->77.1 ).

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