Dataset annotation provides a large amount of labeled data for machine learning. In the dataset production

it needs to draw a box manually for annotation by using the various annotation tools. It is greatly affected by subjective factors. Moreover

due to the complex industrial field environment and unstable image quality

it is difficult to achieve the annotation effect. Therefore

an improved MSR (Multi-scale retinex) steel plate defect dataset enhancement algorithm and an adaptive target box annotation method based on the pixel difference are proposed. Firstly

based on MSR

an adaptive weight calculation method was proposed to automatically determine the weight Wk by calculating the image information entropy without manual adjustment. And the collected defect image was enhanced. Then

it was too much to calculate the pixel difference and extract the target boundary directly for the whole image

so a block calculation method was proposed

and the mean matrix and the second-order difference matrix of each sub block were calculated respectively. By considering the distribution of the target in each sub block

the appropriate sub block was selected to calculate the four boundary of the rectangular box. It assists the defect dataset annotation instead of the manual method. The average IoU is 0.87 and the average detection time is 457 ms

and the average IoU and detection time on the open dataset are 0.84 and 473 ms

respectively. The performance is better than that via the other methods. The detection accuracy of Faster R-CNN and YOLOv5 based on the present algorithm are improved by 4.8% and 5.9% respectively

which can provide datasets with stable quality for the deep learning.