research-focus Research Focus International Scientific Journal Research Focus 2181-3833 Ilm-fan va innovatsiyalar akademiyasi 10.66073/researchfocus.v4i10.1793 REL-A-013110 05.00.00 TRIKOTAJ NUQSONLARINI ANIQLASHDA KONVOLYUTSION NEYRON TARMOQLARI (CNN), QO‘LLAB-QUVVATLOVCHI MASHINA (SVM) VA “RANDOM FOREST” ALGORITMLARINING SOLISHTIRMA TAHLILI COMPARATIVE ANALYSIS OF CONVOLUTIONAL NEURAL NETWORKS (CNN), SUPPORT VECTOR MACHINE (SVM) AND “RANDOM FOREST” ALGORITHMS IN DETECTING KNITTED FABRIC DEFECTS СРАВНИТЕЛЬНЫЙ АНАЛИЗ АЛГОРИТМОВ СВЁРТОЧНЫХ НЕЙРОННЫХ СЕТЕЙ (CNN), МЕТОДА ОПОРНЫХ ВЕКТОРОВ (SVM) И «RANDOM FOREST» ПРИ ОБНАРУЖЕНИИ ДЕФЕКТОВ ТРИКОТАЖА Sherzod Qorabayev Namangan davlat texnika universiteti Xusanxon Bobojanov Jahongir Soloxiddinov Namangan davlat texnika universiteti Sherzod Djurayev Namangan davlat texnika universiteti 05 11 2025 2025 4 10 20 25 https://creativecommons.org/licenses/by/4.0 Ushbu asar Creative Commons Attribution 4.0 International litsenziyasi asosida litsenziyalangan.

ushbu tadqiqot trikotaj matolaridagi nuqsonlarni aniqlash uchun Konvolyutsion Neyron Tarmoqlari (CNN), Qo‘llab-quvvatlovchi Mashina (SVM) va Random Forest algoritmlarining samaradorligini solishtirmalı tahli qilishga bag‘ishlangan. 5000 ta tasvirdan iborat ma‘lumotlar to‘plamida o‘tkazilgan eksperimentlar natijasida CNN modeli 96.8% aniqlik, SVM 89.3% va Random Forest 91.2% natijalarni ko‘rsatgan. Tadqiqot shuni ko‘rsatadiki, yuqori aniqlik talab qilingan vaziyatlarda CNN, hisoblash resurslari cheklangan bo‘lganda esa Random Forest afzalroq hisoblanadi. Ushbu natijalar trikotaj sanoati uchun avtomatlashtirilgan sifat nazorati tizimlarini loyihalashda amaliy ahamiyatga ega.

This study is devoted to a comparative analysis of the effectiveness of Convolutional Neural Networks (CNN), Support Vector Machine (SVM) and Random Forest algorithms for detecting defects in knitted fabrics. As a result of experiments carried out on a dataset consisting of 5000 images, the CNN model showed an accuracy of 96.8%, SVM 89.3% and Random Forest 91.2%. The study shows that CNN is preferable in situations requiring high accuracy, whereas Random Forest is preferable when computational resources are limited. These results are of practical importance for designing automated quality control systems for the knitwear industry.

данное исследование посвящено сравнительному анализу эффективности сверточных нейронных сетей (CNN), метода опорных векторов (SVM) и алгоритма Random Forest для обнаружения дефектов в трикотажных полотнах. Эксперименты, проведенные на наборе данных из 5000 изображений, показали, что модель CNN достигла точности 96.8%, SVM - 89.3%, а Random Forest - 91.2%. Исследование демонстрирует, что CNN предпочтительнее в случаях, требующих высокой точности, тогда как Random Forest более подходит при ограниченных вычислительных ресурсах. Эти результаты имеют практическое значение для проектирования автоматизированных систем контроля качества в трикотажной промышленности.

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