A Corrosion Object Detection Model Based on Yolov8n and Image Preprocess Methods

Material failure is frequently caused by material corrosion. To expedite maintenance by enabling more accurate identification of corroded areas in materials, this study employs a corrosion target detection image dataset, using 12 types of image enhancement and image filtering methods to preprocess the image dataset and using the You Only Look Once (Yolo)v8n model for image detection on each preprocessing dataset. The simulation's findings demonstrate that in contrast to Yolov5n and Yolov6n, Yolov8n can achieve higher Precision, Recall, mAP50, and mAP50-95 values. Simultaneously, maximum Recall and mAP50 values were obtained using color enhancement with a multiplier of 1.5, maximum mAP50-95 value was obtained using median filtering with a convolutional kernel size of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3 \times 3$</tex>, and maximum Precision value was obtained using minimum filtering with a convolutional kernel size of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5 \times 5$</tex>. The above results illustrate that selecting appropriate image preprocessing methods can further improve the yolov8 model for identifying corrosion areas in the image. Thus, these findings can provide a certain reference value for the field of corrosion detection.