Design of AI-Based Detection System for Rock Cuttings Fluorescence of Logging

Existing fluorescent logging detection methods have problems such as single activation light source, limited quantitative evaluation accuracy, and complex detection calculation. To address these issues, an artificial intelligence (AI)-based detection system for rock cuttings fluorescence of logging was developed, so as to detect oily substances rapidly. In order to adapt to the characteristics of different cuttings samples, the lamp source wavelength could be freely adjusted according to the type and area of cuttings, and the cuttings samples could be shot with industrial cameras to collect high-definition images easily detected by deep learning algorithms. An improved DeepLab v3+ algorithm embedded in a mobile device was used to detect cuttings fluorescence, calculate the fluorescence ratio, and display the results and detection images on the mobile device screen. Tests of various cuttings samples show that the system achieves an average intersection over union of 72.73% in detecting cuttings fluorescence, ensuring both accuracy and timeliness while quantifying fluorescent areas in the rock samples. The intelligent detection system for cuttings fluorescence based on the improved DeepLab v3+ algorithm eliminates uncertainties present in manual fluorescence detection processes of cuttings and meets the practical needs of fluorescent logging technology for cuttings fluorescence detection.