Koçdemir, İsmail Hakkı
An important problem in computer vision, particularly in object detection, is being able to perceive objects even under challenging illumination conditions. Being robust to such conditions is especially important in applications, such as autonomous driving. Despite the significance of the problem, existing autonomous driving systems use deep object detection networks with low-dynamic range (LDR) images during both the training phase and the testing phase. In this thesis, we investigate whether high-dynamic range (HDR) images can provide better performance for object detection in autonomous driving systems. For this purpose, we provide a comprehensive analysis of the effect of dynamic range on object detection performance. We compare LDR and HDR images on different illumination conditions and show that HDR performs on par with to LDR counterparts when used without pre-processing including normalization and gamma correction. We also show that after applying this certain pre-processing operations, HDR is able achieve on par detection performance with tone-mapped LDR. Moreover, we propose a novel framework to jointly optimize deep-learning-based tone-mapping operators and object detection networks by using a Generative Adversarial approach. Our architecture achieves the best tone-mapping quality score while maintaining a competitive performance to the best classical tone-mapping operator in terms of detection performance.


Object Detection for Autonomous Driving: High-Dynamic Range vs. Low-Dynamic Range Images
Kocdemir, Ismail H.; Akyüz, Ahmet Oğuz; Koz, Alper; Chalmers, Alan; Alatan, Abdullah Aydın; Kalkan, Sinan (2022-01-01)
An important problem in autonomous driving is to perceive objects even under challenging illumination conditions. Despite this problem, existing solutions use low-dynamic range (LDR) images for object detection for autonomous driving. In this paper, we provide a novel analysis on whether high-dynamic range (HDR) images can provide better performance for object detection for autonomous driving. To this end, we choose a seminal deep object detector and systematically evaluate its performance when trained with...
Object Detection with Minimal Supervision
Demirel, Berkan; Cinbiş, Ramazan Gökberk; İkizler Cinbiş, Nazlı; Department of Computer Engineering (2023-1-18)
Object detection is considered one of the most challenging problems in computer vision since it requires correctly predicting both the object classes and their locations. In the literature, object detection approaches are usually trained in a fully-supervised manner, with a large amount of annotated data for all classes. Since data annotation is costly in terms of both time and labor, there are also alternative object detection methods, such as weakly supervised or mixed supervised learning to reduce these ...
Does estimated depth help object detection?
Çetinkaya, Bedrettin; Akbaş, Emre; Department of Computer Engineering (2019)
With the widespread use of RGB-D cameras, depth information has improved solutions of many computer vision problems including object detection. Object detection can exploit depth information and different encodings obtained from the depth map. Although previous works proved that depth information can be used to improve object detection results, this thesis investigates the effects of depth map to object detection from different aspects in detailed experiments. To clarify these effects, we examine the follow...
Visual object detection and tracking using local convolutional context features and recurrent neural networks
Kaya, Emre Can; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2018)
Visual object detection and tracking are two major problems in computer vision which have important real-life application areas. During the last decade, Convolutional Neural Networks (CNNs) have received significant attention and outperformed methods that rely on handcrafted representations in both detection and tracking. On the other hand, Recurrent Neural Networks (RNNs) are commonly preferred for modeling sequential data such as video sequences. A novel convolutional context feature extension is introduc...
Object recognition and segmentation via shape models
Altınoklu, Metin Burak; Ulusoy, İlkay; Tarı, Zehra Sibel; Department of Electrical and Electronics Engineering (2016)
In this thesis, the problem of object detection, recognition and segmentation in computer vision is addressed with shape based methods. An efficient object detection method based on a sparse skeleton has been proposed. The proposed method is an improved chamfer template matching method for recognition of articulated objects. Using a probabilistic graphical model structure, shape variation is represented in a skeletal shape model, where nodes correspond to parts consisting of lines and edges correspond to pa...
Citation Formats
İ. H. Koçdemir, “AN ANALYSIS ON THE EFFECT OF DYNAMIC RANGE ON OBJECT DETECTION WITH DEEP NEURAL NETWORKS,” M.S. - Master of Science, Middle East Technical University, 2021.