This paper presents a general, trainable system for object detection in unconstrained, cluttered scenes. The system derives much of its power from a representation that describes an object class in terms of an overcomplete dictionary of local, oriented, multiscale intensity differences between adjacent...
A Rapidly Trainable and Global Illumination Invariant Object Detection SystemThis paper addresses the main difficulty in adopting Viola-Jones-type object detection systems: their training time. Large training times are the result of having to repeatedly evaluate thousands of Haar-like features (HFs) in...
By learning many detection algorithms and does not compromise an object class in terms of a subset of an overcomplete the ability of the system to detect non-moving objects. dictionary of wavelet basisfunctions, we derive a com- Initial work on the detection of rigid objects in pact ...
YOLO works to perform object detection in a single stage by first separating the image into N grids. Each of these grids is of equal size SxS. Each of these regions is used to detect and localize any objects they may contain. For each grid, bounding box coordinates, B, for the potential...
Rui Qian, Xin Lai, Xirong Li: 3D Object Detection for Autonomous Driving: A Survey (Pattern Recognition 2022: IF=8.518) - rui-qian/SoTA-3D-Object-Detection
In order to reduce the occurrence of highway incidents, an object detection algorithm for highway intrusion was proposed in this paper. Firstly, a new feature extraction module was proposed to better preserve the main information. Secondly, a new feature fusion method was proposed to improve the...
Low-light object detection is an important research area in computer vision, but it is also a difficult issue. This research offers a low-light target detection network, NLE-YOLO, based on YOLOV5, to address the issues of insufficient illumination and no
Recommendation System (NCF)✅ Object Detection✅ Head to theexamplesdirectory for more details. Other resources to refer to, beyond the examples: Frequently-asked questions (FAQ) Model zoo Compression scheduling Usage Preparing a model for quantization ...
The platform adopts rich coding schemes and a trainable and scalable neural state machine, enabling flexible cooperation of hybrid networks. In addition, an embedded system is developed using a cross-paradigm neuromorphic chip to facilitate the implementation of diverse neural networks in spike or non...
where \(\theta\) is a trainable parameter and \({ReLU}\) is the activation function. The final embedding of \({v}_{t}\) is obtained by stacking information via all \(L\) HGT layers, and \(L\) is set to be 2 in DeepMAPS. 5. Determination of gene to cell attention We call...