Convolutional Neural Network Hierarchical Training for Object Detection
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Solution Overview
Problem
Current object detection systems are limited by smaller datasets compared to classification systems, making it challenging to recognize a wide variety of objects accurately and efficiently, due to the high cost of labeling images for detection.
Innovation Solution
The proposed solution involves using a hierarchical view of object classification and a joint training algorithm to combine detection and classification datasets, allowing convolutional neural networks to be trained on large datasets like ImageNet for classification and COCO for detection, enabling the detection of over 9000 object categories with improved localization and vocabulary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If object detection systems use large datasets like ImageNet for training, then the vocabulary and classification capability improve, but the detection accuracy and localization precision deteriorate due to lack of detection-specific labels
Solution Approach 1:
The training process is segmented into distinct phases: first training on detection datasets with localization labels to learn precise object boundaries, then fine-tuning on classification datasets without localization labels to expand vocabulary. This segmentation allows the system to acquire different capabilities at different stages without interference.
Solution Approach 2:
The system performs preliminary training on detection-specific datasets before expanding to larger classification datasets. This preliminary action establishes strong localization capabilities that are then preserved while adding vocabulary through subsequent fine-tuning on datasets without localization labels.
2Measurement precision
If object detection systems are trained on datasets with localization labels, then the localization accuracy improves, but the computational cost and time for labeling increases significantly
Solution Approach 1:
The system uses partial labeling strategies where only certain datasets require localization labels while others use classification labels only. This partial action reduces the overall labeling burden while still achieving good localization performance through the datasets that do have labels.
Solution Approach 2:
The system copies successful training approaches from classification tasks and adapts them for detection. By using pre-trained classification models and transferring knowledge to detection tasks, the system reduces the need for extensive manual localization labeling while maintaining performance.
3Speed
If object detection systems process images in real-time, then the speed improves, but the accuracy deteriorates due to hardware constraints on wearable devices
Solution Approach 1:
The system implements dynamic processing where the level of analysis can be adjusted based on computational resources available. On resource-constrained wearable devices, the system can perform faster but less thorough analysis, while on more powerful systems, it can perform more comprehensive processing for higher accuracy.
Solution Approach 2:
The detection process is segmented into multiple stages with different computational requirements. Early stages perform fast coarse detection suitable for real-time processing on wearables, while later stages can perform more accurate refinement when computational resources are available, balancing speed and accuracy.
Data Source
AI summary
Systems and methods are disclosed for image-based object detection and classification. For example, methods may include accessing an image from an image sensor; applying a convolutional neural network to the image to obtain localization data to detect an object depicted in the image and to obtain classification data to classify the object, in which the convolutional neural network has been trained in part using training images with associated localization labels and classification labels and has been trained in part using training images with associated classification labels that lack localization labels; annotating the image based on the localization data and the classification data to obtain an annotated image; and storing, displaying, or transmitting the annotated image.