DETECTION OF SMALL AND MEDIUM SIZED SHIPS IN SATELLITE IMAGES USING YOLO MODELS

Mohamed Emara; Yasmeen Abushawareb; Eftal Şehirli

Makale detayları

DETECTION OF SMALL AND MEDIUM SIZED SHIPS IN SATELLITE IMAGES USING YOLO MODELS

Yazarlar:

Mohamed Emara | Yasmeen Abushawareb | Eftal Şehirli

Yayıncı:

Karabuk University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Current Trends in Computing Vol.3 Issue 1, pp.17-27

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350062

pISSN:

2980-3152

Anahtar kelimeler:

Özet:

 Ship detection in satellite images is an essential part of maritime security and surveillance. This work presents a helper tool for its growth. The three models presented in this paper use YOLOv8 and YOLOv5 with colored and grayscale satellite photos to recognize small and medium-sized ships, which are frequently hard to spot in satellite photographs. Performance parameters such as mean average precision mAP, recall, and accuracy were assessed. The models accuracy and recall ranged from 0.87 to 0.93 and 0.84 to 0.86, respectively, according to the results. This work uncovered a comparable performance for both grayscale and colored images and acceptable detection accuracy. In satellite images, Model 2 and Model 3 demonstrated efficacy in identifying small and medium-sized ships.

 Ship detection in satellite images is an essential part of maritime security and surveillance. This work presents a helper tool for its growth. The three models presented in this paper use YOLOv8 and YOLOv5 with colored and grayscale satellite photos to recognize small and medium-sized ships, which are frequently hard to spot in satellite photographs. Performance parameters such as mean average precision mAP, recall, and accuracy were assessed. The models accuracy and recall ranged from 0.87 to 0.93 and 0.84 to 0.86, respectively, according to the results. This work uncovered a comparable performance for both grayscale and colored images and acceptable detection accuracy. In satellite images, Model 2 and Model 3 demonstrated efficacy in identifying small and medium-sized ships.

 Ship detection in satellite images is an essential part of maritime security and surveillance. This work presents a helper tool for its growth. The three models presented in this paper use YOLOv8 and YOLOv5 with colored and grayscale satellite photos to recognize small and medium-sized ships, which are frequently hard to spot in satellite photographs. Performance parameters such as mean average precision mAP, recall, and accuracy were assessed. The models accuracy and recall ranged from 0.87 to 0.93 and 0.84 to 0.86, respectively, according to the results. This work uncovered a comparable performance for both grayscale and colored images and acceptable detection accuracy. In satellite images, Model 2 and Model 3 demonstrated efficacy in identifying small and medium-sized ships.

 Ship detection in satellite images is an essential part of maritime security and surveillance. This work presents a helper tool for its growth. The three models presented in this paper use YOLOv8 and YOLOv5 with colored and grayscale satellite photos to recognize small and medium-sized ships, which are frequently hard to spot in satellite photographs. Performance parameters such as mean average precision mAP, recall, and accuracy were assessed. The models accuracy and recall ranged from 0.87 to 0.93 and 0.84 to 0.86, respectively, according to the results. This work uncovered a comparable performance for both grayscale and colored images and acceptable detection accuracy. In satellite images, Model 2 and Model 3 demonstrated efficacy in identifying small and medium-sized ships.

Makale detayları

DETECTION OF SMALL AND MEDIUM SIZED SHIPS IN SATELLITE IMAGES USING YOLO MODELS

Yazarlar:

Mohamed Emara | Yasmeen Abushawareb | Eftal Şehirli

Yayıncı:

Karabuk University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Current Trends in Computing Vol.3 Issue 1, pp.17-27

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350062

pISSN:

2980-3152

Ship detection

YOLO

satellite images

image preprocessing

maritime surveillance.

Sayı

Makale detayları

DETECTION OF SMALL AND MEDIUM SIZED SHIPS IN SATELLITE IMAGES USING YOLO MODELS

Yazarlar: Mohamed Emara | Yasmeen Abushawareb | Eftal Şehirli

Yayıncı: Karabuk University

Ana Başlıklar: Artificial Intelligence (Other)

Dergi: Current Trends in Computing Vol.3 Issue 1, pp.17-27

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001350062

pISSN: 2980-3152

Ship detection

YOLO

satellite images

image preprocessing

maritime surveillance.

Sayı

Yazarlar:

Mohamed Emara | Yasmeen Abushawareb | Eftal Şehirli

Yayıncı:

Karabuk University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Current Trends in Computing Vol.3 Issue 1, pp.17-27

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350062

pISSN:

2980-3152