What Makes for Effective Detection Proposals?

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Object detection models commonly use proposal methods to optimize search efficiency, avoiding exhaustive image scanning.
• The specific trade-offs associated with these proposal methods in object detection remain largely unexamined.

Purpose of the Study:

• To conduct an in-depth analysis of twelve proposal methods and four baselines for object detection.
• To evaluate proposal repeatability, ground truth recall on PASCAL, ImageNet, and MS COCO datasets.
• To assess the impact of proposal methods on DPM, R-CNN, and Fast R-CNN detection performance.

Main Methods:

• Analyzed twelve proposal methods and four baselines.
• Evaluated proposal repeatability and ground truth annotation recall.
• Assessed the impact on DPM, R-CNN, and Fast R-CNN object detection frameworks.
• Introduced a novel metric, Average Recall (AR), to quantify proposal quality.

Main Results:

• Improving proposal localization accuracy is as critical as improving recall for object detection.
• The proposed Average Recall (AR) metric demonstrates a strong correlation with actual detection performance.
• Identified common strengths and weaknesses across various existing proposal methods.

Conclusions:

• The study provides crucial insights into the performance trade-offs of object detection proposal methods.
• Offers practical metrics for selecting and optimizing proposal techniques.
• Highlights the importance of both recall and localization accuracy in proposal generation.