Invention Application
- Patent Title: EFFICIENT OBJECT DETECTION USING DEEP LEARNING TECHNIQUES
-
Application No.: US17512049Application Date: 2021-10-27
-
Publication No.: US20220147748A1Publication Date: 2022-05-12
- Inventor: Soyeb Nagori , Deepak Poddar
- Applicant: TEXAS INSTRUMENTS INCORPORATED
- Applicant Address: US TX Dallas
- Assignee: TEXAS INSTRUMENTS INCORPORATED
- Current Assignee: TEXAS INSTRUMENTS INCORPORATED
- Current Assignee Address: US TX Dallas
- Priority: IN202041049581 20201112
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06K9/32 ; G06N3/04

Abstract:
Various embodiments of the present technology relate to using neural networks to detect objects in images. More specifically, some embodiments relate to the reduction of computational analysis regarding object detection via neural networks. In an embodiment, a method of performing object detection is provided. The method comprises determining, via a convolution neural network, at least a classification of an image, wherein the classification corresponds to an object in the image and comprises location vectors corresponding to pixels of the image. The method also comprises, for at least a location vector of the location vectors, obtaining a confidence level, wherein the confidence level represents a probability of the object being present at the location vector, and calculating an upper-bound score based at least on the confidence level. The method further comprises, for at least an upper-bound score based at least on the confidence level, performing an activation function on the upper-bound score, and classifying, via a detection layer, the object in the image.
Public/Granted literature
- US12243300B2 Efficient object detection using deep learning techniques Public/Granted day:2025-03-04
Information query