Geri Dön

Inventory counting by a deep learning based object detection model in stock yard of an urban furniture manufacturer

Kent mobilyaları üretim firmasına ait stok sahasında derin öğrenme tabanlı bir nesne algılama modeli ile stok sayımı

PDF İndir

  1. Tez No: 895626
  2. Yazar: SERDAR KAPLAN
  3. Danışmanlar: DR. ÖĞR. ÜYESİ ABDULLAH HULUSİ KÖKÇAM
  4. Tez Türü: Yüksek Lisans
  5. Konular: Endüstri ve Endüstri Mühendisliği, Industrial and Industrial Engineering
  6. Anahtar Kelimeler: Derin Öğrenme, Evrişimli Sinir Ağları, Nesne Algılama, YOLO, Envanter Sayımı, Envanter Sayım Araçları, Kent Mobilyaları Envanter sayımı
  7. Yıl: 2024
  8. Dil: İngilizce
  9. Üniversite: Sakarya Üniversitesi
  10. Enstitü: Fen Bilimleri Enstitüsü
  11. Ana Bilim Dalı: Endüstri Mühendisliği Ana Bilim Dalı
  12. Bilim Dalı: Endüstri Mühendisliği Bilim Dalı
  13. Sayfa Sayısı: 95

Özet

Özet yok.

Özet (Çeviri)

Inventory counting is a critical financial process for organizations, as it ensures accurate reporting of raw materials, components, and finished products within production and logistics operations. Precise inventory counts are essential for preventing financial losses that arise from discrepancies between recorded and actual quantities. Accurate counts help avoid issues such as overstocking, which leads to unnecessary expenses, and understocking, which can disrupt production processes. The use of computer vision in inventory counting offers a promising approach to enhancing accuracy and efficiency, addressing the limitations inherent in traditional methods. Manual inventory counting, while effective for small quantities of neatly arranged items, becomes increasingly problematic with larger and more complex stockpiles. The process is labor-intensive and time-consuming, particularly when dealing with extensive volumes of diverse products. Traditional methods such as Radio Frequency Identification (RFID) and barcode/QR code scanning face significant limitations. RFID signals can be obstructed by materials like concrete, and barcode/QR code labels are susceptible to weather conditions and adherence issues, leading to inaccuracies in counting and inefficiencies in inventory management. To address these challenges, this study explores the application of computer vision and deep learning techniques, specifically Convolutional Neural Networks (CNNs), to improve inventory counting. The methodology involved creating a unique dataset of images of selected products, categorized into four classes, and performing object labeling on hundreds of images. Data augmentation techniques were employed to enhance the robustness of the model. The YOLOv10 (You Only Look Once version 10) model was then trained using the augmented dataset to perform object detection and counting. The results of the study indicated Mean Average Precision(mAP) score of %86.9 that the YOLOv10 model successfully counted products in various scenarios, demonstrating the potential of computer vision to enhance inventory accuracy. Metrics and visualizations revealed that the model was effective in certain contexts, though limitations were observed, particularly in handling complex inventory scenes and specific product types. The study concludes that while computer vision and deep learning offer significant improvements over traditional methods, further refinements and additional research are needed to address the identified challenges and optimize inventory counting processes.

Benzer Tezler

  1. Tez No

    643754

    Semi - autonomous warehouse counting system

    Yarı otonom depo sayım sistemi

    GİZEM MELİKE CİDAL

    Yüksek Lisans

    İngilizce

    İngilizce

    2020

    Mekatronik MühendisliğiYıldız Teknik Üniversitesi

    Mekatronik Mühendisliği Ana Bilim Dalı

    DR. ÖĞR. ÜYESİ HÜSEYİN ÜVET

  2. Tez No

    606593

    Türkiye yaban hayatı geliştirme sahası envanterlerinin değerlendirilmesi

    Evaluation of Turkey wildlife development areas inventory

    AYGÜN KÖSE ÇITLAK

    Yüksek Lisans

    Türkçe

    Türkçe

    2019

    Ormancılık ve Orman Mühendisliğiİstanbul Üniversitesi-Cerrahpaşa

    Orman Mühendisliği Ana Bilim Dalı

    DOÇ. DR. ZEYNEL ARSLANGÜNDOĞDU

  3. Tez No

    694913

    Maliyet kontrolü açısından tıbbi malzemelerin standart maliyet yöntemine göre varyans analizi: Bir üniversite hastanesi örneği

    Analysis of variance of medical materials according to standard cost method in terms of cost control: A university hospital example

    FİGEN ÇİVİ

    Yüksek Lisans

    Türkçe

    Türkçe

    2021

    Sağlık Kurumları YönetimiDüzce Üniversitesi

    Sağlık Yönetimi Ana Bilim Dalı

    PROF. DR. ENVER BOZDEMİR

  4. Tez No

    546579

    İlkokul dördüncü sınıf öğrencilerinin akıcı okuma becerilerini geliştirmede tekerlemelerin etkisi

    The effect of rhymes on the development of fluent reading skills of 4th graders in primary school

    ZEYNEP YILMAZ ALKAN

    Yüksek Lisans

    Türkçe

    Türkçe

    2019

    Eğitim ve ÖğretimOrdu Üniversitesi

    Temel Eğitim Ana Bilim Dalı

    DOÇ. DR. KEZİBAN TEKŞAN

  5. Tez No

    390906

    İnmeli hastalarda ziyaret sürecinin vital bulgular ve anksiyete üzerine etkisi

    The effect of visitings on vital signs and anxiety of the stroke patients

    NURDAN CAMBAZTEPE

    Yüksek Lisans

    Türkçe

    Türkçe

    2014

    HemşirelikGATA

    İç Hastalıkları Hemşireliği Ana Bilim Dalı

    DOÇ. DR. HATİCE ÇİÇEK

Geri Dön