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Deep learning object detectors often return false positives with very high confidence. Although they optimize generic detection performance, such as mean average precision (mAP), they are not designed for reliability. For a re- liable detection system, if a high confidence detection is made, we would want high certainty that the object has indeed been detected. To achieve this, we have developed a set of verification tests which a proposed detection must pass to be accepted. We develop a theoretical framework which proves that, under certain assumptions, our verification tests will not accept any false positives. Based on an approximation to this framework, we present a practical detection system that can verify, with high precision, whether each detection of a machine-learning based object detector is correct. We show that these tests can improve the overall accu- racy of a base detector and that accepted examples are highly likely to be correct. This allows the detector to operate in a high precision regime and can thus be used for robotic perception systems as a reliable instance detection method.
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A Strawberry Detection System Using Convolutional Neural Networks
In recent years, robotic technologies, e.g. drones or autonomous cars have been applied to the agricultural sectors to improve the efficiency of typical agricultural operations. Some agricultural tasks that are ideal for robotic automation are yield estimation and robotic harvesting. For these applications, an accurate and reliable image-based detection system is critically important. In this work, we present a low-cost strawberry detection system based on convolutional neural networks. Ablation studies are presented to validate the choice of hyper- parameters, framework, and network structure. Additional modifications to both the training data and network structure that improve precision and execution speed, e.g., input compression, image tiling, color masking, and network compression, are discussed. Finally, we present a final network implementation on a Raspberry Pi 3B that demonstrates a detection speed of 1.63 frames per second and an average precision of 0.842.
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- Award ID(s):
- 1757787
- PAR ID:
- 10095111
- Date Published:
- Journal Name:
- 5th National Symposium for NSF REU Research in Data Science, Systems, and Security
- Page Range / eLocation ID:
- 2515 to 2520
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData.2018.8622466
