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- W4382564384 abstract "Deep learning was used to detect boulders that can damage excavators in opencast mines. Different network architectures were applied, i.e., modern YOLOv5, RetinaNet and Mask-RCNN. Studies were carried out in which the results obtained using a few networks were compared. The abovementioned neural networks were exploited in a framework for detection of oversized boulders on a conveyor belt operating in an opencast coal mine. The method is based on the analysis of a certain number of consecutive frames of the film from an industrial camera. The novelty relies on checking the detection of a boulder within subsequent frames and allowing the skipping of a prescribed small number of neighboring frames with false negative detections. This allows one to make a decision about stopping a conveyor belt after detecting a boulder in consecutive frames even when they are interleaved with frames that contained a boulder missed by a detector due to misleading environmental conditions such as shadows or sand. The method was tested on recordings from an opencast mine in Poland. The proposed method can help prevent the failure of expensive equipment." @default.
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- W4382564384 date "2023-01-01" @default.
- W4382564384 modified "2023-10-01" @default.
- W4382564384 title "Detection of Objects Dangerous for the Operation of Mining Machines" @default.
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- W4382564384 doi "https://doi.org/10.1007/978-3-031-36027-5_10" @default.
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