Deep Learning Based Multiple Object Detection
نویسندگان
چکیده
منابع مشابه
Deep Learning-Based Man-Made Object Detection from Hyperspectral Data
Hyperspectral sensing, due to its intrinsic ability to capture the spectral responses of depicted materials, provides unique capabilities towards object detection and identification. In this paper, we tackle the problem of man-made object detection from hyperspectral data through a deep learning classification framework. By the effective exploitation of a Convolutional Neural Network we encode ...
متن کاملDeep learning for class-generic object detection
We investigate the use of deep neural networks for the novel task of class-generic object detection. We show that neural networks originally designed for image recognition can be trained to detect objects within images, regardless of their class, including objects for which no bounding box labels have been provided. In addition, we show that bounding box labels yield a 1% performance increase o...
متن کاملHierarchical Object Detection with Deep Reinforcement Learning
We present a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent. The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller wi...
متن کاملMelanoma detection with a deep learning model
Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions. Methods: In this analytic s...
متن کاملMultiple Component Learning for Object Detection
Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, achieving very low false positives rates. The field has also seen a resurgence of part-based recognition methods, with impressive results on highly articulated, diverse object categories. In this paper we propose a discriminativ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2021
ISSN: 1742-6588,1742-6596
DOI: 10.1088/1742-6596/1916/1/012225