Object Detection Network Training with Landmark Loss
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The robustness of object detection networks is poor due to challenges in accurately detecting target objects, especially in complex scenarios with interfering objects and varying lighting conditions, which affects detection accuracy and reliability.
Innovation Solution
A method and apparatus for training an object detection network that calculates a total loss function incorporating detection class, detection position, and landmark position losses, and adjusts network parameters to improve robustness, using techniques such as convolutional networks, color enhancement, and landmark detection, to enhance detection accuracy and reduce interference from complex backgrounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object detection networks are used, then the detection process is simple, but the robustness and detection accuracy are poor in complex scenarios
Solution Approach 1:
The detection network is segmented into multiple functional modules: a detection box prediction module that predicts bounding boxes, a landmark prediction module that predicts key point positions, and a loss calculation module that computes multiple loss functions. This segmentation allows each module to specialize in specific tasks, improving overall robustness while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent extends the detection task from two dimensions (bounding box location and class) to four dimensions by adding landmark position predictions (x, y coordinates for multiple key points). This dimensional expansion provides richer semantic information about the target object, enhancing detection reliability in complex scenarios without significantly increasing network structural complexity.
2Measurement precision
If detection only uses bounding box position, then the detection process is simple, but the detection accuracy is insufficient for complex scenarios
Solution Approach 1:
The patent applies local quality by predicting landmark positions at specific key points on the target object (such as facial features or body joints) rather than treating the object as a uniform entity. This localized detailed prediction enhances detection precision for specific critical regions while keeping the overall network complexity manageable through focused feature extraction at landmark locations.
Solution Approach 2:
The patent replaces simple geometric bounding box detection with a more sophisticated system that incorporates semantic landmark positioning. Instead of relying solely on mechanical coordinate prediction for box corners, the system uses neural network-based semantic understanding to predict meaningful key point locations, thereby improving detection accuracy through substitution of a more intelligent detection mechanism.
3Reliability
If the network parameters are not adjusted using comprehensive loss functions, then the training process is simple, but the detection quality is poor in varied lighting conditions
Solution Approach 1:
The patent creates a composite loss function that integrates multiple loss components: detection box loss, landmark position loss, and classification loss. This composite loss function combines different optimization objectives into a unified training framework, enabling the network to simultaneously learn bounding box prediction, landmark positioning, and class classification, thereby improving detection reliability in varied conditions through multi-faceted optimization.
Solution Approach 2:
The patent implements feedback mechanisms through the loss function calculation that continuously evaluates detection box accuracy, landmark position accuracy, and classification correctness. This feedback is used to adjust network parameters during training, creating a closed-loop optimization process that improves detection reliability by systematically reducing errors across multiple detection dimensions based on performance feedback.
Data Source
AI summary
A method of training an object detection network, an object detection method and related apparatuses are provided. The training method includes: inputting a training image into a to-be-trained object detection network to obtain detection information of a target object, where the detection information includes a detection class of the target object, a detection position of a detection box of the target object, and a detection position of a landmark of the target object; calculating a total loss function of the to-be-trained object detection network, where the total loss function is calculated according to a loss function of the detection class of the target object, a loss function of the detection position of the detection box of the target object, and a loss function of the detection position of the landmark of the target object; and adjusting a parameter of the to-be-trained object detection network according to the total loss function.