Author Archives: IDENTIFYBRE

With the sensor specific encasing complete, all that was left was to put those two sub-assemblies together and have an enclosure to hold the raspberry PI, breadboard and all the electrical wires. A few aspects of this design: Airflow was required to keep the electrical components at a stable level so slots were added toContinue reading “Final Mechanical Design”

Welcome to the sixth and final instalment of the overview series! We will be discussing the next steps. The textile classification sensor as it is right now is great for many reasons as discussed in the fifth instalment. In order to improve the following steps will be taken. Work will be done to: Sort betweenContinue reading “Overview Series: The Next Steps”

Welcome back to the overview series! In the fifth instalment the results from the benefits of the current design of the sensor are discussed. During the brainstorming and prototyping of this project, multiple designs were considered. In the end a classification sensor was decided on as the one the team will pursue. The following areContinue reading “Overview Series: The Benefits”

Welcome back to the overview series! In the fourth instalment the final results from the computer vision model is discussed.  Our final iteration of the computer vision model is a ResNet50 multi-class classification model that classifies between cotton, denim, and polyester. During training, regularization and data augmentations (blur, affine, etc) were used to artificially increaseContinue reading “Overview Series: The Prototype Results”

After the initial testing prototype. The team was ready to put the camera sensing and IR sensing together. Using the 3D printed parts from the last design, the team put the parts together to make a make-shift demo prototype. This prototype was functional and able to detect clothing. This prototype will be used to demoContinue reading “Prototype 2: A clearer vision”

Welcome back to the overview series! Here is the third instalment that goes through the solution developed. The textile classification sensor can be broken down into three main components: Mechanical, electrical, and software.  The mechanical design of the sensor is a portable, custom-fit, aesthetically pleasing design that was 3D-printed. After initial designs, a few iterationsContinue reading “Overview Series: The Solution”

Welcome back to the overview series! Here is the second instalment that goes through the main problems we found. Fast fashion has propelled a culture of continuous buying and throwing away of clothing. Rather than the traditional 2 cycles per year, fast fashion puts out an average 50 cycles per year of new clothing lines,Continue reading “Overview Series: The Problem”

This is the first post in the overview series which will be a series that summarizes the journey the group went to in order to create the final textile classification sensor! The journey to choosing this project started off with the desire to create something that would help to make the world a better place.Continue reading “Overview Series: The Objective”

Attempts were made to improve the accuracy of the detection of wool using computer vision but the average recall remained around ~40% on the validation dataset. We suspect this is largely due to the lack of original samples for wool (only 90 in our dataset) in addition to the similarity of the texture of woolContinue reading “Replacing Wool with Denim in CV”

Now that an initial prototype was complete, the team decided that 3D printing would be the best way to custom-make encasings for each of the sensors. The IR sensor would have it’s own encasing specific to it’s features. The camera similarly would have it’s own encasing. The camera encasing was left at 2 pieces. OneContinue reading “Mechanical Design: 3D printed encasings”