Zone Evaluation: Revealing Spatial Bias in Object Detection.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Object detectors face a fundamental limitation known as spatial bias, leading to reduced performance on objects near image borders.
• Existing methods lack effective ways to measure, identify, and understand the origins and extent of spatial bias.
• This bias results in uneven detection performance across different image zones.

Purpose of the Study:

• To introduce a novel zone evaluation protocol for quantifying spatial bias in object detectors.
• To investigate the underlying causes of spatial bias beyond object scale and absolute position.
• To highlight the need for addressing spatial disequilibrium in object detection models.

Main Methods:

• Developed a zone evaluation protocol to measure detection performance across image zones, yielding Zone Precisions (ZPs).
• Conducted heuristic experiments to explore factors influencing spatial bias.
• Evaluated 10 popular object detectors across 5 diverse detection datasets.

Main Results:

• Object detectors demonstrate significantly uneven performance across different image zones.
• Performance in the 96% border zone did not reach the overall Average Precision (AP).
• Spatial bias is primarily driven by imperceptible data pattern divergences between zones, not object scale or position.

Conclusions:

• Spatial bias is a critical issue in object detection, stemming from subtle data pattern variations.
• The proposed zone evaluation protocol provides a quantitative measure of this bias.
• Future research should focus on mitigating spatial disequilibrium to achieve robust and balanced detection performance across all image regions.