What makes for effective detection proposals? (1502.05082v3)

Abstract: Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and widespread use of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in-depth analysis of twelve proposal methods along with four baselines regarding proposal repeatability, ground truth annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM, R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object detection improving proposal localisation accuracy is as important as improving recall. We introduce a novel metric, the average recall (AR), which rewards both high recall and good localisation and correlates surprisingly well with detection performance. Our findings show common strengths and weaknesses of existing methods, and provide insights and metrics for selecting and tuning proposal methods.

• The paper introduces the Average Recall (AR) metric, which provides a comprehensive evaluation of proposal quality over traditional metrics.
• The analysis reveals that grouping methods like SelectiveSearch and MCG offer high-quality proposals with robust recall and repeatability across datasets.
• Integrating proposals with detectors such as Fast R-CNN enhances detection performance and highlights the need for hybrid methods in future research.

An Expert Review of "What Makes for Effective Detection Proposals?"

The paper "What Makes for Effective Detection Proposals?" by Jan Hosang, Rodrigo Benenson, Piotr Dollár, and Bernt Schiele provides a comprehensive analysis of a wide range of detection proposal methods employed in modern object detection systems. This research is pivotal in understanding the trade-offs related to these methods, especially with the shift from exhaustive sliding window searches to more efficient detection proposals.

Overview of Detection Proposal Methods

The paper explores twelve contemporary detection proposal methods, alongside four baselines, evaluating them on key metrics such as proposal repeatability, recall of ground truth annotations across datasets like PASCAL VOC, ImageNet, and MS COCO, and their impact on the detection performance when integrated with detectors like DPM, R-CNN, and Fast R-CNN.

Two main categories of detection proposals are considered:

1. Grouping Methods: These techniques, including SelectiveSearch, CPMC, and MCG, generate proposals by segmenting the image into regions likely to contain objects. They tend to be computationally intensive but provide high-quality proposals.
2. Window Scoring Methods: Methods like EdgeBoxes and Bing score potential bounding boxes based on certain heuristics to predict the presence of objects. These methods are generally faster but may lack the high localization precision of grouping methods.

Key Findings and Metrics

One of the core contributions of this paper is the introduction of the Average Recall (AR) metric, which is shown to correlate well with the detection performance, better than traditional metrics. AR evaluates the recall over a range of intersection over union (IoU) thresholds from 0.5 to 1, providing a more comprehensive view of proposal quality. This metric is advocated as a new standard for evaluating detection proposals.

Evaluation Highlights

1. Repeatability: Most proposal methods exhibit significant sensitivity to small image perturbations, with SelectiveSearch and EdgeBoxes striking a balance between stability and object recall. High repeatability is essential for robust performance since detectors should consistently identify similar object boundaries under slight variations.
2. Recall: On the PASCAL VOC 2007 dataset, methods like MCG, EdgeBoxes, SelectiveSearch, Rigor, and Geodesic perform well, with MCG generally leading across different metrics. Importantly, these methods demonstrate their robustness across the more extensive ImageNet and MS COCO datasets, thus showing no significant bias toward specific training sets.
3. Detection Performance: Integrating the detection proposals with detectors reveals that SelectiveSearch, Rigor, MCG, and EdgeBoxes consistently yield high detection performance. These results hold true across different detectors, including DPM, R-CNN, and Fast R-CNN.

Procedural Efficiency in Detection

By testing the detectors with and without proposals, it is evident that while detection proposals significantly reduce the computational load compared to sliding window methods, there is a nuanced interplay between proposal recall and localization quality. Indeed, the experiments suggest that improving localization accuracy remains as critical as high recall for optimizing detection performance.

Implications and Future Directions

The research underscores the importance of detection proposals in advancing object detection. Insights from this paper could drive further optimization of proposal methods, particularly in enhancing repeatability and integrating more robust feature utilization from advances in convolutional neural networks (CNNs). The potential for adapting proposal methods to maximize the AR metric specifically promises better object detection systems more closely aligned with practical application needs.

Future research may explore new hybrid methods that combine the high recall rates of grouping strategies with the computational efficiency of window scoring methods. Additionally, exploring top-down approaches and better integrating proposal methods with object detectors could further enhance proposal effectiveness.

Conclusion

This paper is a critical step in guiding the development and refinement of detection proposals. By providing a thorough evaluation framework and introducing the AR metric, it sets a new benchmark for subsequent research in object detection. As detection proposals continue to evolve, tools and practices from this paper will be pivotal in shaping the field, pushing towards faster, more accurate, and robust object detection systems.