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.


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...
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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...
A Confidence Ranked Co-Occurrence Approach for Accurate Object Recognition in Highly Complex Scenes
Angın, Pelin (2013-01-01)
Real-time and accurate classification of objects in highly complex scenes is an important problem for the Computer Vision community due to its many application areas. While boosting methods with the sliding window approach provide fast processing and accurate results for particular object categories, they cannot achieve the desired performance for more involved categories of objects. Recent research in Computer Vision has shown that exploiting object context through relational dependencies between object ca...
Efficient detection and tracking of salient regions for visual processing on mobile platforms
Serhat, Gülhan; Saranlı, Afşar; Department of Electrical and Electronics Engineering (2009)
Visual Attention is an interesting concept that constantly widens its application areas in the field of image processing and computer vision. The main idea of visual attention is to find the locations on the image that are visually attractive. In this thesis, the visually attractive regions are extracted and tracked in video sequences coming from the vision systems of mobile platforms. First, the salient regions are extracted in each frame and a feature vector is constructed for each one. Then Scale Invaria...
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.