MoogleLabs Introduces Revolutionary Screen Damage Detection System, Transforming Mobile Insurance Claims

MoogleLabs’ screen damage detection system uses AI and ML to automate evaluation, revolutionizing mobile insurance claim processing. It reduces manual effort, improves efficiency, achieves 90%+ accuracy in damage classification, and detects cross-device compatibility and makes/model information.

Mississauga, Ontario, Canada, 5th Jul 2023, King NewsWire – MoogleLabs, a leading technology innovator and a trusted AI ML services provider has announced the launch of their groundbreaking screen damage detection system. Leveraging advanced artificial intelligence and machine learning algorithms, this cutting-edge solution is all set to revolutionize how mobile insurance companies evaluate screen damages, and process claims.

Traditional assessment methods for screen damage have long relied on manual inspection, resulting in time-consuming processes and potential human errors. With MoogleLabs’ screen damage detection system, mobile insurance companies can automate and expedite the evaluation process, significantly improving efficiency and customer experience.

The technical prowess behind this brand-new system lies in its utilization of state-of-the-art technologies, such as a VGG image classification model. In addition to that, they have fine-tuned the system to identify mobile screens in images and accurately detect cracks with remarkable precision.

Through seamless integration of the OpenCV library, the software ensures efficient handling of image processing tasks, facilitating seamless reading and manipulation of images. The incorporation of the Canny edge detection technique further enhances the system’s ability to assess the probability of cracks in mobile screens accurately.

Notably, the screen damage detection system comes with a user-friendly web interface powered by the Flask Framework, allowing insurance companies to utilize the system effortlessly.

Some key highlights of this screen damage detection system include:

Accurate Assessment: The system achieves over 90% accuracy in screen damage classification, ensuring reliable and precise evaluation.

Reduced Manual Effort: By automating the visual inspection and classification process, manual effort is significantly reduced, eliminating the need for time-consuming and error-prone manual tagging.

Cross-Device Compatibility: The system is trained to detect and grade screen damage across various mobile phone models and screen types, providing efficiencies of scale and higher throughput.

Make and Model Detection: In addition to damage assessment, the system also identifies the make and model of the phone, facilitating categorization for further workflows and repair cost assessment.

MoogleLabs’ screen damage detection system sets a new standard in mobile insurance claim processing, enabling insurance companies to enhance their operations, improve decision-making, and deliver superior customer service.

About MoogleLabs

MoogleLabs is a trusted AI ML services provider based in Canada that offers information technology solutions to businesses that want to leverage the latest technologies for benefit. With their aim of ‘decoding innovation’, they offer services to all sizes and types of businesses and entrepreneurs with top-notch ideas.

As a startup, they have already helped several businesses realize their dreams through extensive research and optimum products in a variety of technologies, including AI, ML, Blockchain, Metaverse, DevOps, and more.

Media Contact

Organization: MoogleLabs

Contact Person: Ganesh Verma

Website: https://www.mooglelabs.com/

Email: info@mooglelabs.com

Contact Number: +1(209) 201-0654

Address: 55 Village Centre Place Suite 307, Mississauga Ontario L4Z1V9, Canada

Address 2: 6398, 166 street, Surrey BC, V3S0W4, Canada

City: Mississauga

State: Ontario

Country: Canada

Release Id: 0507234526

The post MoogleLabs Introduces Revolutionary Screen Damage Detection System, Transforming Mobile Insurance Claims appeared first on King Newswire.

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