Training object detectors by directly optimizing lrp metric

Download

12625703.pdf

Date

2020-9

Author

Çam, Barış Can

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

394
views

448
downloads

This thesis focuses on training deep object detection networks by directly optimizing the localisation-recall-precision (LRP) performance metric that can evaluate classification and localisation performance of an object detector in a unified manner (Oksuz et al., 2018). To achieve this goal, unlike the commonly used linear weighting approach, we aim to implicitly optimize the LRP metric first by using a bounded localisation loss from previous works and proposing a loss function that can bound the range of classification task loss. In addition to this range balancing approach, we aim to train an object detector with an LRP regressor trained with LRP values collected during the training stage. We show that the proposed regression architecture can estimate LRP values with low error rates. However, training an object detector by attaching the regressor architecture as a differentiable LRP error estimator did not yield satisfactory results. Finally, by adapting the perceptron learning algorithm based approach proposed by Chen et al. (2020), we show that we can embed the LRP metric as a loss function to train a deep object detector. In this thesis, this perceptron learning-based approach is examined, and its generalization to all IoU based localisation loss functions is proposed.

Subject Keywords

Ranking based loss functions, Training object detectors, Evaluation of object detectors, Deep learning, Object detection

URI

https://hdl.handle.net/11511/69201

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Reliable Low Resolution 01-4DM Receivers via Deep Learning Balevi, Eren; Andrews, Jeffrey G. (2018-01-01) This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption, but makes accurate data detection difficult. This is particularly true for multicarrier waveforms, which have high peak-to-average ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe dist...
IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES Kaya, Emre Can; Alatan, Abdullah Aydın (2018-10-10) A novel extension to proposal-based detection is proposed in order to learn convolutional context features for determining boundaries of objects better. Objects and their context are aimed to be learned through parallel convolutional stages. The resulting object and context feature maps are combined in such a way that they preserve their spatial relationship. The proposed algorithm is trained and evaluated on PASCAL VOC 2007 detection benchmark dataset and yielded improvements in performance over baseline, ...
Weakly supervised instance attention for multisource fine-grained object recognition with an application to tree species classification Aygunes, Bulut; Cinbiş, Ramazan Gökberk; Aksoy, Selim (2021-06-01) Multisource image analysis that leverages complementary spectral, spatial, and structural information benefits fine-grained object recognition that aims to classify an object into one of many similar subcategories. However, for multisource tasks that involve relatively small objects, even the smallest registration errors can introduce high uncertainty in the classification process. We approach this problem from a weakly supervised learning perspective in which the input images correspond to larger neighborh...
Representation Learning for Contextual Object and Region Detection in Remote Sensing Firat, Orhan; Can, Gulcan; Yarman Vural, Fatoş Tunay (2014-08-28) The performance of object recognition and classification on remote sensing imagery is highly dependent on the quality of extracted features, amount of labelled data and the priors defined for contextual models. In this study, we examine the representation learning opportunities for remote sensing. First we attacked localization of contextual cues for complex object detection using disentangling factors learnt from a small amount of labelled data. The complex object, which consists of several sub-parts is fu...
One-Bit OFDM Receivers via Deep Learning Balevi, Eren; Andrews, Jeffrey G. (2019-06-01) This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization reduces greatly the complexity and power consumption but makes accurate channel estimation and data detection difficult. This is particularly true for multicarrier waveforms that have high peak-to-average power ratio in the time domain and fragile subcarrier orthogonality in the fre...

Citation Formats

B. C. Çam, “Training object detectors by directly optimizing lrp metric,” M.S. - Master of Science, Middle East Technical University, 2020.