Sheep Detection, Tracking and counting from aerial images using Deep Learning

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Object detection is widely used in the field of computer vision. Furthermore, it can be harnessedin agriculture and farming, especially with the new methods that achieve promising results. Nowadays, the problem is tackled using either traditional machine learning methods that use computervision techniques or deep learning methods. In this work, we investigate the deep learning stateof-the-art tools to create a smart system for detecting, tracking and counting sheep using aerialimages captured by a drone. In the process, we gather sufficient data with good quality and use itto train a model dependent on the YOLOv4 network. Next, we tackle the counting stage directlyusing an innovative method that uses an imaginary line cutting the processed frame incrementingthe counter whenever an intersection between the bounding box and the gate happens. However, we had to introduce an intermediate stage because of low performance. That intermediary is called tracking. The results obtained by the experiment are highly promising in detection with an mAPof 71% and 16.1274 % of avg loss function.

روابط وملفات

التعريف والنوع

رقم الوثيقة

fd587fb0-ffa2-409d-99be-add58e370d78

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

ماستر (LMD)

نوع الملف

pdf text

أسماء الملفات

2199746_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":" Abderrahmane Adjila ","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Fatima Djekaba","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"Mohammed Elmehdi Mehaya","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

English

المصدر والدورية

اسم المصدر

Sheep Detection, Tracking and counting from aerial images using Deep Learning

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Object detection, Object tracking, Object counting, deep learning, YOLO, Deep SORT, Aerial images, sheep.

إشراف وإعداد

الإشراف

Abderrahmane Adjila

الإعداد

Fatima Djekaba, Mohammed Elmehdi Mehaya

الاقتباسات الببليوغرافية