Alpaslan Kibar
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Alpaslan Kibar has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Deniz Demircioğlu Diren
Sakarya Üniversitesi
1 shared publication - Dilruba Şahin
Industrial Engineering Department, Sakarya University, Sakarya 54050, Türkiye
1 shared publication - Merve Şişci
Sakarya University, Türkiye
1 shared publication - Orhan Torkul
Sakarya University
1 shared publication - Recep Yilmaz
Sakarya Üniversitesi
1 shared publication
Latest Publications
- Open Access
Real-Time Classification of Chicken Parts in the Packaging Process Using Object Detection Models Based on Deep Learning
Chicken meat plays an important role in the healthy diets of many people and has a large global trade volume. In the chicken meat sector, in some production processes, traditional methods are used. Traditional chicken part sorting methods are often manual and time-consuming, especially during the packaging process. This study aimed to identify and classify the chicken parts for their input during the packaging process with the highest possible accuracy and speed. For this purpose, deep-learning-based object detection models were used. An image dataset was developed for the classification models by collecting the image data of different chicken parts, such as legs, breasts, shanks, wings, and drumsticks. The models were trained by the You Only Look Once version 8 (YOLOv8) algorithm variants and the Real-Time Detection Transformer (RT-DETR) algorithm variants. Then, they were evaluated and compared based on precision, recall, F1-Score, mean average precision (mAP), and Mean Inference Time per frame (MITF) metrics. Based on the obtained results, the YOLOv8s model outperformed the other models developed with other YOLOv8 versions and the RT-DETR algorithm versions by obtaining values of 0.9969, 0.9950, and 0.9807 for the F1-score, mAP@0.5, and mAP@0.5:0.95, respectively. It has been proven suitable for real-time applications with an MITF value of 10.3 ms/image.
Open AccessReal-Time Classification of Chicken Parts in the Packaging Process Using Object Detection Models Based on Deep Learning
Chicken meat plays an important role in the healthy diets of many people and has a large global trade volume. In the chicken meat sector, in some production processes, traditional methods are used. Traditional chicken part sorting methods are often manual and time-consuming, especially during the packaging process. This study aimed to identify and classify the chicken parts for their input during the packaging process with the highest possible accuracy and speed. For this purpose, deep-learning-based object detection models were used. An image dataset was developed for the classification models by collecting the image data of different chicken parts, such as legs, breasts, shanks, wings, and drumsticks. The models were trained by the You Only Look Once version 8 (YOLOv8) algorithm variants and the Real-Time Detection Transformer (RT-DETR) algorithm variants. Then, they were evaluated and compared based on precision, recall, F1-Score, mean average precision (mAP), and Mean Inference Time per frame (MITF) metrics. Based on the obtained results, the YOLOv8s model outperformed the other models developed with other YOLOv8 versions and the RT-DETR algorithm versions by obtaining values of 0.9969, 0.9950, and 0.9807 for the F1-score, mAP@0.5, and mAP@0.5:0.95, respectively. It has been proven suitable for real-time applications with an MITF value of 10.3 ms/image.