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Garbage classification is difficult to supervise in the stage of collection and transportation. This paper proposes a computer vision-based method for intelligent supervision and workload statistics of garbage trucks. In terms of hardware, this paper deploys a camera and an image processing unit with NPU based on the original on-board computing and communication equipment. In terms of software, this paper uses the YOLOv3-tiny algorithm on the image processing unit to perform real-time target detection on garbage truck work, collects statistics on the color, specifications, and quantity of garbage bins cleaned by the garbage truck, and uploads the results to the server for recording and display. The proposed method has low deployment and maintenance costs while maintaining excellent accuracy and real-time performance, which makes it have good commercial application value.

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Manual fracture identification methods based on cores and image logging pseudopictures are limited by the expense and the amount of data. In this paper, we propose an integrated workflow, which takes the fracture identification as an end-to-end project, to combine the boundary detection and the deep learning classification to recognize fractured zones with accurate locations and reasonable thickness. We first apply the discrete wavelet transform algorithm and a boundary detection method named changing point detection to enhance the fracture sensibility of acoustic logs and segment the whole logging interval into non-overlapping subsections by estimating boundaries. The deep neural network based auto-encoders and the convolutional neural network classifier are then implemented to extract the hidden information from logs and categorize the subsections as the fractured or non-fractured zones. To validate the feasibility of this workflow, we apply it to the logging data from a real well. Compare with the benchmarks provided by the support vector machine , random forest and Adaboost model, the one-dimensional well profile predicted by the proposed changing point detection-deep learning classifier is more consistent with the manual identification result.

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