Simple Fusion of Object Detectors for Improved Performance and Faster Deployment

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Object detectors often suffer from multiple performance limitations which may be attenuated with larger training datasets, improved training techniques, and complex detection models. However, such strategies are complex and time-consuming for applications requiring fast deployments. We propose a Simple Fusion of Object Detectors (SFOD) late ensemble method to combine existing pre-trained, off-the-shelf, fine-tuned object detectors and leverage on their divergences to improve the overall detection performance. Comprehensive experimental evaluations, based on PASCAL VOC07 challenge, demonstrate SFOD's ability to improve mean average precision ( ${mAP}$ ) for different fusion sizes and base detector combinations, reaching an absolute 84.08% ${mAP}$ and an improvement of 3.97% ${mAP}$. The improvements extend to most classes, fusion sizes, and base detector combinations, revealing $AP$ improvements up to 17.35% over baselines, for particular object classes. Practical application evaluations, based on optimal threshold selection, also reveal improvements of 10.54% and 8.36% of mean recall ( $mR$ ) and ${mAP}$ , respectively. Our approach does not require additional training and is quickly deployable, yet providing a few adjustable hyperparameters to optimize the recall-precision relation for specific applications. Improvements obtained from our proposed SFOD fusion pipeline span across a broad range of object classes and are important for a wide variety of critical applications where every successful detection is treasured.

頁(從 - 到)33235-33254
期刊IEEE Access
出版狀態Published - 2021


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