Moving Object Detection in Video with Algorithms YOLO and Faster R-CNN in Different Conditions

Year 2022, , 40 - 54, 31.01.2022

Abdulghani Mawlood A.ghani Abdulghani Gonca Gökçe Menekşe Dalveren

https://doi.org/10.31590/ejosat.1013049

Cited By: 1

Abstract

In this study, YOLOv4 with YOLOv3 and Faster R-CNN compared for object detection both under challenging weather conditions and in the dark. It is difficult to detect moving objects such as pedestrians, cars, buses, and motorcycles in bad weather conditions or at night, especially in adverse weather conditions such as rain, fog, and snow. The objective of this study is to assess the performance of these three algorithms in such circumstances, as none of them were designed to work in bad weather or at night. Tesla P4 GPUs with 12GB of RAM were used for this study, with algorithms trained using open-image datasets, where YOLOv4 had the highest performance at 40,000 iterations, 72% mAP, and 63% recall. While YOLOv3 has achieved maximum at 36000 iterations, 65.53% mAP, and 54% recall, Faster R-CNN has achieved maximum at 36,000 iterations, 51% mAP, and 49% recall. According to the results, YOLOv4 performed the best compared to YOLOv3 and Faster R-CNN.

Keywords

Object detection, YOLOv4, YOLOv3, Faster R-CNN

Farklı Koşullarda YOLO ve Faster R-CNN Algoritmaları ile Videoda Hareketli Nesne Tespiti

Abstract

Bu çalışmada, hem zorlu hava koşullarında hem de karanlıkta nesne tespiti için YOLOv4'ü YOLOv3 ve Faster R-CNN ile karşılaştırıldı. Yayalar, arabalar, otobüsler, motosikletler gibi hareketli nesneleri kötü hava koşullarında veya geceleri, özellikle yağmur, sis, kar gibi olumsuz hava koşullarında tespit etmek zordur. Bu çalışmanın amacı, hiçbiri kötü hava koşullarında veya gece çalışmak üzere tasarlanmamış olan bu üç algoritmanın bu gibi durumlarda performansını değerlendirmektir. YOLOv4'ün 40.000 yineleme, %72 mAP ve %63 geri çağırmada en yüksek performansa sahip olduğu açık-görüntü veri kümeleri kullanılarak eğitilen algoritmalarla bu çalışma için 12 GB RAM'e sahip Tesla P4 GPU'lar kullanıldı. YOLOv3 36.000 yineleme, %65.53 mAP ve %54 geri çağırma ile maksimuma ulaşırken, Faster R-CNN 36.000 yineleme, %51 mAP ve %49 geri çağırma ile maksimuma ulaştı. Sonuçlara göre YOLOv4, YOLOv3 ve Faster R-CNN'ye kıyasla en iyi performansı gösterdi.

Keywords

Nesne Tespiti, YOLOv4, YOLOv3, Faster R-CNN

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