This system uses the innovative AIoT application to target annoying scalp maintenance problemsdevelops an intelligent scalp detectionmanagement system. The core of the technology is to use deep learning-based object detection models. Tens of thousands of microscopic image data sets that are labeledtrained for scalp symptoms. As a result, the scalp image recognition module is develop, such as dandruff, hair loss, oil, and inflammation, etc. Hence this system can provide scalp detection, and maintenance effectiveness tracking functions lead scalp maintenance services to a new level of intelligent management.

"The scientific breakthrough of this system is described in 3 parts as follows:
1. Different from similar systems on the market that uses image processing technology as the core only fixednon-symptomatic characterization test items.
2. At present, this system creates the industry＇s unique scalp symptom microscopic feature object image data set. 
3. This work can be used as an example of cross-domain intelligent networking application R&D engineering, primarily through the joint research and development of cross-field experts in AI scalp symptom interpretation technology.

-It reduces the high cost and time of education and training for scalp hair physiotherapists.
-It reduces the mistakes and inconsistent judgments of different human interpreters.
-It provides an automatic and highly accurate AI-based recognition method whereby people can know their current status of scalp hair health problems.
-The diagnosis result from each scalp hair inspection can be sent to an online cloud-based management platform, which can help related enterprises (such as scalp hair therapy and beauty salons) track the progress of scalp hair care, treatments, and customer therapies. 
-It maintains cloud-based scalp hair records for customers, helping scalp hair physiotherapist regularly track and analyze the scalp hair health status their customers