Adaptively Dense Feature Pyramid Network for Object Detection
نویسندگان
چکیده
منابع مشابه
Template Matching based Object Detection Using HOG Feature Pyramid
The Object detection based on histogram of oriented gradients was introduced in the context of detecting humans in images [1]. Detecting human subject in the image is a difficult task due to the fact that they appear in different appearance and poses. Thus the need to come up with a feature that allows human dorm to be detected cleanly even in cluttered background under different illumination. ...
متن کاملSingle-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
Recent years have witnessed many exciting achievements for object detection using deep learning techniques. Despite achieving significant progresses, most existing detectors are designed to detect objects with relatively lowquality prediction of locations, i.e., often trained with the threshold of Intersection over Union (IoU) set to 0.5 by default, which can yield low-quality or even noisy det...
متن کاملConvolutional Gating Network for Object Tracking
Object tracking through multiple cameras is a popular research topic in security and surveillance systems especially when human objects are the target. However, occlusion is one of the challenging problems for the tracking process. This paper proposes a multiple-camera-based cooperative tracking method to overcome the occlusion problem. The paper presents a new model for combining convolutiona...
متن کاملFisher Discriminant Analysis (FDA), a supervised feature reduction method in seismic object detection
Automatic processes on seismic data using pattern recognition is one of the interesting fields in geophysical data interpretation. One part is the seismic object detection using different supervised classification methods that finally has an output as a probability cube. Object detection process starts with generating a pickset of two classes labeled as object and non-object and then selecting ...
متن کاملA Pyramid CNN for Dense-Leaves Segmentation
Automatic detection and segmentation of overlapping leaves in dense foliage can be a difficult task, particularly for leaves with strong textures and high occlusions. We present Dense-Leaves, an image dataset with ground truth segmentation labels that can be used to train and quantify algorithms for leaf segmentation in the wild. We also propose a pyramid convolutional neural network with multi...
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: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2922511