Correlation of Object Detection Performance with Visual Saliency and Depth Estimation
Matthias Bartolo, Dylan Seychell
2024-11-06
Summary
This paper explores how the performance of object detection systems relates to two important visual tasks: depth estimation and visual saliency prediction.
What's the problem?
Object detection systems need to accurately identify and locate objects in images, but they often struggle with complex scenes. Understanding how different visual tasks, like predicting depth (how far away things are) and visual saliency (what stands out in an image), can help improve these systems is essential to making them better.
What's the solution?
The researchers conducted experiments using advanced models on well-known datasets to see how well object detection accuracy correlates with visual saliency and depth prediction. They found that visual saliency had a stronger connection to object detection performance than depth estimation. This means that focusing on what stands out in an image could be more beneficial for improving object detection than just knowing how far away things are. They also noticed that larger objects were easier to detect than smaller ones, suggesting that different strategies might be needed depending on the size of the objects.
Why it matters?
This research is important because it provides insights into how to design better object detection systems by using visual saliency features. By understanding these relationships, developers can create more efficient and accurate models, which can be applied in areas like self-driving cars, security systems, and robotics.
Abstract
As object detection techniques continue to evolve, understanding their relationships with complementary visual tasks becomes crucial for optimising model architectures and computational resources. This paper investigates the correlations between object detection accuracy and two fundamental visual tasks: depth prediction and visual saliency prediction. Through comprehensive experiments using state-of-the-art models (DeepGaze IIE, Depth Anything, DPT-Large, and Itti's model) on COCO and Pascal VOC datasets, we find that visual saliency shows consistently stronger correlations with object detection accuracy (mArho up to 0.459 on Pascal VOC) compared to depth prediction (mArho up to 0.283). Our analysis reveals significant variations in these correlations across object categories, with larger objects showing correlation values up to three times higher than smaller objects. These findings suggest incorporating visual saliency features into object detection architectures could be more beneficial than depth information, particularly for specific object categories. The observed category-specific variations also provide insights for targeted feature engineering and dataset design improvements, potentially leading to more efficient and accurate object detection systems.