HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES

Ahmet Ornek; Murat Ceylan

doi:10.17780/ksujes.1430479

TR EN

FARKLI ÖLÇEKLERDE ZAYIF DENETİMLİ NESNE TESPİTİ İÇİN HAYCAM VE EIGEN KARŞILAŞTIRILMASI

Öz

Açıklanabilir Yapay Zeka yaklaşımlarından biri olan Sınıf Aktivasyon haritaları ile bir sınıflama işlemi gerçekleştirildiği zaman, giriş görüntüsü üzerindeki sınıflamaya etki eden alanlar ortaya çıkarılabilmektedir. Yani bir sınıflayıcı modelin görüntünün hangi kısmına bakarak karar verdiği gösterilmektedir. Bu çalışmada açık kaynak bir veri seti olan CUB 200 2011 kullanılarak 200 sınıflı bir sınıflama modeli eğitilmiş ve sınıflama sonuçları EigenCAM ve HayCAM yöntemleri kullanılarak görselleştirilmiştir. Sınıflamaya etki eden alanlar kullanılarak gerçekleştirilen nesne tanıma performansları karşılaştırıldığında EigenCAM yöntemi %30.88 IoU değerine ulaşırken HayCAM yöntemi %41.95 değerine ulaşmaktadır. Elde edilen çıktılar Temel Bileşenler Analizi kullanılarak elde edilen sonuçların (HayCAM), Tekil Değer Ayrışımı kullanılarak elde edilen sonuçlardan (EigenCAM) daha iyi olduğunu göstermektedir.

Anahtar Kelimeler

HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES

Abstract

When a classification process is performed using Class Activation Maps, which is one of the Explainable Artificial Intelligence approaches, the areas influencing the classification on the input image can be revealed. In other words, it is demonstrated which part of the image the classifier model looks at to make a decision. In this study, a 200-class classification model was trained using the open-source dataset CUB 200 2011, and the classification results were visualized using the EigenCAM and HayCAM methods. When comparing object detection performances based on the areas influencing classification, the EigenCAM method reaches an IoU (Intersection over Union) value of 30.88%, while the HayCAM method reaches a value of 41.95%. The obtained results indicate that outputs derived using Principal Component Analysis (HayCAM) are better than those obtained using Singular Value Decomposition (EigenCAM).

Keywords

Destekleyen Kurum

Huawei Türkiye Ar-Ge Merkezi

Etik Beyan

Makale, yazarların kendi araştırmalarını ve analizlerini güvenilir ve eksiksiz bir şekilde yansıtmaktadır.

Teşekkür

Huawei Türkiye Ar-Ge Merkezi

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Görüşü , Görüntü İşleme , Derin Öğrenme

Bölüm

Araştırma Makalesi

Yazarlar

Ahmet Ornek ^*
0000-0001-7254-9316
Türkiye

Murat Ceylan
0000-0001-6503-9668
Türkiye

Yayımlanma Tarihi

3 Eylül 2024

Gönderilme Tarihi

2 Şubat 2024

Kabul Tarihi

22 Temmuz 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 27 Sayı: 3

DOI

https://doi.org/10.17780/ksujes.1430479

IZ

https://izlik.org/JA78LA67FG

Kaynak Göster

RIS / Bibtex

APA

Ornek, A., & Ceylan, M. (2024). HAYCAM VS EIGENCAM FOR WEAKLY-SUPERVISED OBJECT DETECTION ACROSS VARYING SCALES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 1078-1088. https://doi.org/10.17780/ksujes.1430479