International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com K Siva Venkata Madhav 6 1 (January - February) 2024 01 January-February enData preprocessingTraffic sign detectionTraffic sign recognition and Transmission Detecting and recognizing traffic signs automatically is vital for efficiently managing trafficsign inventory with minimal human intervention While existing methods in the computer vision field excel at recognizing signs pertinent to advanced driverassistance and autonomous systems they cover only a fraction of the total traffic sign categories leaving a significant portion unaddressed This gap poses a challenge for automating trafficsign inventory management which requires handling a broader range of signs In our study we tackle this challenge by employing a convolutional neural network CNN approach specifically the mask RCNN to handle the entire detection and recognition process in a seamless manner We propose several enhancements to improve the detection performance which we evaluate on a dataset comprising 200 different traffic sign categories including those not previously explored These findings indicate the feasibility of deploying our approach in realworld applications for trafficsign inventory management Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2024 All Rights Reserved