Scrapify
Digitalising ScrapDealing
The problem Scrapify solves
Scrapify addresses a significant issue in the traditional scrap-selling process, where individuals and households often struggle with inefficiency, lack of transparency, and poor pricing. In the conventional system, people typically rely on local scrap dealers, who may not always offer fair prices, and users often have no real bargaining power or understanding of the scrap’s market value. Additionally, the process can be time-consuming, inconvenient, and lacks the flexibility to compare multiple dealers or options.
For scrap dealers, the problem extends to missed opportunities for sourcing valuable scrap from a larger pool of sellers. Many dealers face difficulties in finding consistent suppliers or ensuring the quality and quantity of scrap materials. On both ends, the system lacks digital infrastructure, causing inefficiencies, limited access to real-time market data, and potential exploitation for sellers.
Scrapify solves these issues by creating an online platform that connects users with a network of scrap dealers. It introduces transparency by offering users the ability to compare dealers based on price, reviews, and service quality. Sellers no longer have to rely on a single dealer but can choose based on competitive offerings, ensuring they get the best possible deal. The platform also incentivizes users with "Green Points" to encourage more scrap selling and promote sustainable practices.
For scrap dealers, Scrapify provides access to a broader customer base, improving business opportunities and streamlining operations through digital transactions, route optimization, and efficient scrap sourcing. In essence, Scrapify transforms a disorganized, opaque market into an efficient, transparent, and eco-friendly digital ecosystem.
Challenges we ran into
In the Ideation we had problem in whether to connect scrapdealers with users or Recycling Units. After many rounds of discussion we thought to keep Scrapdealers independent.
Now During Implementation,
PROBLEM :- We ran into problem that system might encounter new or rare types of scrap materials that were not included in the original training dataset, leading to incorrect or incomplete classifications.
SOLUTION : - We established a feedback loop where users can report misclassifications or upload new types of scrap materials. This feedback is used to collect additional training data, which is then incorporated into periodic model retraining. We also have a manual review process to handle cases where the model is unsure, ensuring that users receive accurate information even for new materials.
PROBLEM :- Users may upload images with varying qualities, such as different resolutions, lighting conditions, or angles, which can affect the accuracy of the image recognition system.
SOLUTION :- To address this, we implemented a robust preprocessing pipeline that normalizes images before they are fed into the model. This pipeline includes steps such as resizing, adjusting brightness and contrast, and filtering noise. Additionally, we used data augmentation techniques during training to help the model generalize better across different image conditions.
PROBLEM :- Handling user-uploaded images involves managing sensitive data and ensuring privacy and security.
SOLUTION :- We implemented strong encryption for data transmission and storage. Images are anonymized and used solely for improving the system’s performance. We also comply with data protection regulations such as GDPR, and we provide clear privacy policies and options for users to manage their data.
Tracks Applied (1)
Polygon Track
Polygon