Damaged Parcel Detection in the Logistics Sector Using the Yolov12 Model with Roboflow 3.0

Ahmet Rodoplu; İncilay Yıldız

Makale detayları

Damaged Parcel Detection in the Logistics Sector Using the Yolov12 Model with Roboflow 3.0

Yazarlar:

Ahmet Rodoplu | İncilay Yıldız

Yayıncı:

Istanbul University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 2, pp.142-154

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356122

pISSN:

3062-2697

Anahtar kelimeler:

Özet:

 This study presents an automated system for detecting physical damage to packages in logistics operations using deep learning. Manual inspections are often slow and error prone, whereas deep learning based object detection, particularly the YOLO You Only Look Once family, offers both speed and accuracy for real time applications. We created a dataset of 5,188 images labelled as Damaged or Undamaged on the Roboflow 3.0 platform. To enhance the robustness, each image was augmented with greyscale conversion and 90 rotations clockwise and counterclockwise. The dataset was divided into 86 training, 7 validation, and 7 test sets and used to train the YOLOv12 model. The model achieved a mean average precision at IoU  0.50 mAP0.50 of 93.7, with a Precision of 93.3 and a Recall of 93.8 on the test set. Integrated into a Python GUI, the model processes user selected images in real time, drawing bounding boxes around packages and labelling their damage status. This approach significantly improves the operational efficiency and detection accuracy by reducing the reliance on manual inspections. Future work will investigate advanced augmentation techniques e.g., varying lighting, contrast, and shadows and transfer learning to enhance generalisation across diverse package types and environmental conditions.

 This study presents an automated system for detecting physical damage to packages in logistics operations using deep learning. Manual inspections are often slow and error prone, whereas deep learning based object detection, particularly the YOLO You Only Look Once family, offers both speed and accuracy for real time applications. We created a dataset of 5,188 images labelled as Damaged or Undamaged on the Roboflow 3.0 platform. To enhance the robustness, each image was augmented with greyscale conversion and 90 rotations clockwise and counterclockwise. The dataset was divided into 86 training, 7 validation, and 7 test sets and used to train the YOLOv12 model. The model achieved a mean average precision at IoU  0.50 mAP0.50 of 93.7, with a Precision of 93.3 and a Recall of 93.8 on the test set. Integrated into a Python GUI, the model processes user selected images in real time, drawing bounding boxes around packages and labelling their damage status. This approach significantly improves the operational efficiency and detection accuracy by reducing the reliance on manual inspections. Future work will investigate advanced augmentation techniques e.g., varying lighting, contrast, and shadows and transfer learning to enhance generalisation across diverse package types and environmental conditions.

 This study presents an automated system for detecting physical damage to packages in logistics operations using deep learning. Manual inspections are often slow and error prone, whereas deep learning based object detection, particularly the YOLO You Only Look Once family, offers both speed and accuracy for real time applications. We created a dataset of 5,188 images labelled as Damaged or Undamaged on the Roboflow 3.0 platform. To enhance the robustness, each image was augmented with greyscale conversion and 90 rotations clockwise and counterclockwise. The dataset was divided into 86 training, 7 validation, and 7 test sets and used to train the YOLOv12 model. The model achieved a mean average precision at IoU  0.50 mAP0.50 of 93.7, with a Precision of 93.3 and a Recall of 93.8 on the test set. Integrated into a Python GUI, the model processes user selected images in real time, drawing bounding boxes around packages and labelling their damage status. This approach significantly improves the operational efficiency and detection accuracy by reducing the reliance on manual inspections. Future work will investigate advanced augmentation techniques e.g., varying lighting, contrast, and shadows and transfer learning to enhance generalisation across diverse package types and environmental conditions.

 This study presents an automated system for detecting physical damage to packages in logistics operations using deep learning. Manual inspections are often slow and error prone, whereas deep learning based object detection, particularly the YOLO You Only Look Once family, offers both speed and accuracy for real time applications. We created a dataset of 5,188 images labelled as Damaged or Undamaged on the Roboflow 3.0 platform. To enhance the robustness, each image was augmented with greyscale conversion and 90 rotations clockwise and counterclockwise. The dataset was divided into 86 training, 7 validation, and 7 test sets and used to train the YOLOv12 model. The model achieved a mean average precision at IoU  0.50 mAP0.50 of 93.7, with a Precision of 93.3 and a Recall of 93.8 on the test set. Integrated into a Python GUI, the model processes user selected images in real time, drawing bounding boxes around packages and labelling their damage status. This approach significantly improves the operational efficiency and detection accuracy by reducing the reliance on manual inspections. Future work will investigate advanced augmentation techniques e.g., varying lighting, contrast, and shadows and transfer learning to enhance generalisation across diverse package types and environmental conditions.

Makale detayları

Damaged Parcel Detection in the Logistics Sector Using the Yolov12 Model with Roboflow 3.0

Yazarlar:

Ahmet Rodoplu | İncilay Yıldız

Yayıncı:

Istanbul University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 2, pp.142-154

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356122

pISSN:

3062-2697

Object Detection

Artificial Intellegence

Deep Learning

Logistics

Sayı

Makale detayları

Damaged Parcel Detection in the Logistics Sector Using the Yolov12 Model with Roboflow 3.0

Yazarlar: Ahmet Rodoplu | İncilay Yıldız

Yayıncı: Istanbul University

Ana Başlıklar: Artificial Intelligence (Other)

Dergi: Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 2, pp.142-154

Belge Tipi: Makale

Yayın Tarihi: 7/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001356122

pISSN: 3062-2697

Object Detection

Artificial Intellegence

Deep Learning

Logistics

Sayı

Yazarlar:

Ahmet Rodoplu | İncilay Yıldız

Yayıncı:

Istanbul University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 2, pp.142-154

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356122

pISSN:

3062-2697