Accelerating Two-Stage Object Detectors for On-Device Inference in Remote Sensing

Core Concepts

A model simplification method is proposed to accelerate two-stage object detectors for on-device inference in remote sensing, by utilizing only one feature extraction and applying a high-pass filter, while maintaining accuracy.

Abstract

The paper proposes a method to simplify two-stage object detectors for on-device inference in remote sensing applications. The key highlights are: The authors identify that the computational complexity of the regression part in two-stage detectors is a major bottleneck for on-device inference, even after techniques like pruning. To address this, the authors remove the feature pyramid structure and perform regression using only a single feature. This significantly reduces the computational load in the RPN, NMS, and RoIAlign stages. To compensate for the accuracy drop from using a single feature, the authors make two key modifications: a. They adjust the anchor sizes to better match the object sizes in the remote sensing dataset. b. They apply a high-pass filter to the RPN's score map to focus on small objects. The authors evaluate their method on state-of-the-art two-stage detectors like ReDet, Oriented-RCNN, and LSKNet on the DOTA-v1.5 dataset. Their approach reduces computation costs by up to 61.2% while maintaining accuracy within 2.7% of the baseline. The authors show that selecting the right single feature and designing the high-pass filter are crucial for maintaining accuracy. Their method is generally applicable to any two-stage detector using a feature pyramid network.

Stats

The DOTA-v1.5 dataset has 2,806 images and 403,318 instances across 16 object classes. The input image size is commonly 1024x1024 pixels, cropped from the original images ranging from 800 to 4,000 pixels in width and height.

Quotes

"Providing real-time performance is difficult due to the computational complexity of high-accuracy object detection models." "Our approach is advantageous because it is applicable to any two-stage detector with a feature pyramid network."

Key Insights Distilled From

Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing

by Jaemin Kang,... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07405.pdf

Deeper Inquiries

How can the proposed method be extended to one-stage detectors to achieve further efficiency gains

To extend the proposed method to one-stage detectors for increased efficiency gains, several adjustments and considerations can be made. One-stage detectors typically rely on a single feature for object detection, similar to the approach proposed in the study for two-stage detectors. By focusing on optimizing this single feature selection process, the efficiency of one-stage detectors can be enhanced. Additionally, incorporating anchor size adjustments based on the specific characteristics of the dataset can further improve the detection accuracy of one-stage detectors. By fine-tuning the anchor sizes to match the objects in the dataset, the detector can better capture the diverse range of object sizes present in remote sensing images. Furthermore, implementing a high-pass filter in one-stage detectors can help enhance the detection of small objects while reducing computational overhead. By carefully designing and applying the high-pass filter, the detector can maintain accuracy levels while efficiently detecting objects of varying sizes. Overall, by adapting the proposed method to one-stage detectors with a focus on feature selection, anchor size adjustments, and high-pass filtering, significant efficiency gains can be achieved in object detection tasks for remote sensing applications.

What are the potential limitations or drawbacks of using a high-pass filter, and how can they be addressed

While the high-pass filter used in the proposed method can effectively enhance the detection of small objects and improve accuracy in certain cases, there are potential limitations and drawbacks to consider. One limitation is the risk of introducing noise or false positives in the detection process. The high-pass filter may amplify certain features in the image, leading to an increase in scores for regions that do not actually contain objects of interest. This can result in misclassifications and reduced overall detection accuracy. To address this limitation, the design of the high-pass filter should be carefully optimized to minimize the amplification of noise while enhancing the detection of relevant objects. Additionally, the threshold for applying the high-pass filter should be fine-tuned to ensure that it effectively targets small objects without introducing excessive noise. Regular validation and testing of the filter's performance on diverse datasets can help mitigate these limitations and ensure accurate object detection results.

Can the anchor size adjustment and feature selection be further automated or optimized for different remote sensing datasets

The anchor size adjustment and feature selection processes can be further automated or optimized for different remote sensing datasets to enhance the efficiency and accuracy of object detection models. One approach to automate anchor size adjustment is to implement adaptive anchor sizing algorithms that dynamically adjust anchor sizes based on the distribution of object sizes in the dataset. By analyzing the statistics of object sizes in the dataset, the detector can automatically determine the optimal anchor sizes for different object categories, improving detection performance. Additionally, feature selection can be optimized by incorporating machine learning techniques to identify the most informative features for object detection. By training models to select the most relevant features based on their discriminative power, the detector can efficiently utilize the most valuable information for accurate object detection. Furthermore, leveraging transfer learning and domain adaptation techniques can help optimize feature selection for specific remote sensing datasets, ensuring that the detector focuses on the most relevant features for object detection tasks. By automating and optimizing anchor size adjustment and feature selection processes, object detection models can be tailored to different datasets, leading to improved performance and efficiency in remote sensing applications.