Deep Convolutional Neural Network for Storefront Detection
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Solution Overview
Problem
Accurately identifying and segmenting business storefronts in street-level photographs is challenging due to high intra-class variability, ambiguity in physical extent, and large scale, making manual annotation prohibitive and existing automated methods inefficient.
Innovation Solution
A deep convolutional neural network is used for end-to-end learning to predict bounding boxes and confidence scores, integrating proposal generation and post-processing, with a distributed implementation to enhance runtime efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to identify business storefronts, then accuracy can be maintained, but the process becomes prohibitively time-consuming and expensive at large scale
Solution Approach 1:
The system uses automatically generated bounding boxes from the trained neural network as proposals for refinement, rather than relying entirely on manual annotation. The network learns to self-correct and improve its own detections through the training process, reducing the need for extensive manual labeling while maintaining high accuracy.
Solution Approach 2:
The system performs preliminary automated detection using the trained convolutional neural network to generate bounding box proposals before any refinement or verification steps. This preliminary action filters out obvious non-matches and focuses subsequent computational resources on ambiguous cases, dramatically reducing the overall time required compared to pure manual annotation.
2Measurement precision
If traditional object detection methods are used, then comprehensive search is possible, but the computational complexity and processing time become prohibitive
Solution Approach 1:
The system replaces traditional mechanical object detection approaches (sliding windows, feature matching, hierarchical classification) with a端到端 convolutional neural network that directly maps image regions to bounding boxes and class predictions. This substitution leverages learned features and end-to-end optimization to achieve both high accuracy and computational efficiency, processing speeds up to 1000x faster than traditional methods.
Solution Approach 2:
The system changes the fundamental parameters of the detection approach by using a neural network that directly outputs bounding box coordinates and confidence scores in a single pass, rather than sequentially applying multiple detection stages. This parameter change in the detection pipeline enables real-time processing while maintaining high precision through the network's learned decision boundaries.
3Productivity
If existing automated methods are used, then scalability is achieved, but accuracy and reliability remain insufficient for practical applications
Solution Approach 1:
The system incorporates feedback mechanisms where the neural network's predictions are evaluated against ground truth data during training, and the loss function guides continuous improvement of detection accuracy. The confidence scores generated by the network provide feedback on prediction reliability, allowing the system to focus computational resources on uncertain cases and maintain high overall reliability at scale.
Solution Approach 2:
The system employs dynamic refinement where the neural network adapts its detection thresholds and confidence requirements based on the specific image context and detected object characteristics. This dynamic adjustment allows the system to maintain high reliability across diverse scenarios while preserving fast processing speeds, rather than using fixed conservative thresholds that would slow down processing.
Data Source
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AI summary
Aspects of the present disclosure relate to a method includes training a deep neural network (310) using training images (320) and data (330) identifying one or more business storefront locations in the training images. The deep neural network (310) outputs tight bounding boxes (340) on each image. At the deep neural network (310), a first image (510) may be received. The first image (510) may be evaluated using the deep neural network (310). Bounding boxes (522, 524, 526, 528) may then be generated identifying business storefront locations in the first image (520).