Towards Spatial Equilibrium Object Detection
Zhaohui Zheng, Yuming Chen, Qibin Hou, Xiang Li, Ming-Ming Cheng

TL;DR
This paper investigates the spatial bias in object detection, introduces a new measurement called Spatial equilibrium Precision (SP), and proposes a spatial equilibrium label assignment (SELA) to improve detection robustness by addressing spatial disequilibrium.
Contribution
The paper presents a novel analysis of spatial bias in object detection, introduces the SP metric, and proposes SELA to mitigate spatial disequilibrium, enhancing detection performance.
Findings
Spatial imbalance significantly impacts detection performance.
The proposed SP metric effectively characterizes spatial detection performance.
SELA improves robustness across multiple datasets.
Abstract
Semantic objects are unevenly distributed over images. In this paper, we study the spatial disequilibrium problem of modern object detectors and propose to quantify this ``spatial bias'' by measuring the detection performance over zones. Our analysis surprisingly shows that the spatial imbalance of objects has a great impact on the detection performance, limiting the robustness of detection applications. This motivates us to design a more generalized measurement, termed Spatial equilibrium Precision (SP), to better characterize the detection performance of object detectors. Furthermore, we also present a spatial equilibrium label assignment (SELA) to alleviate the spatial disequilibrium problem by injecting the prior spatial weight into the optimization process of detectors. Extensive experiments on PASCAL VOC, MS COCO, and 3 application datasets on face mask/fruit/helmet images…
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Remote-Sensing Image Classification
