Correlation of Object Detection Performance with Visual Saliency and
  Depth Estimation
Authors: name: Matthias Bartolo, name: Dylan Seychell

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.