@article{LUO2023, 
author = {Cai LUO and Jihua WU and Shixin SUN and Kaiyang LI and Peng REN},
title = {Experiment scheme design for underwater transparent organisms detection based on fusion of event frames and RGB frames},
year = {2023},
journal = {Experimental Technology and Management},
volume = {40},
number = {4},
pages = {62-68},
keywords = {underwater transparent organisms detection, event camera, YOLOX, ASFF (adaptive spatial feature fusion), loss function},
url = {https://www.sciopen.com/article/10.16791/j.cnki.sjg.2023.04.008},
doi = {10.16791/j.cnki.sjg.2023.04.008},
abstract = {In order to improve the detection accuracy of underwater transparent organisms, in the aspect of image preprocessing, it is proposed to use the event frame converted by the event camera and RGB frame for pixel-level fusion of the image, in order to strengthen the edge features of underwater transparent organisms. In terms of detection, the improved YOLOX algorithm is proposed for underwater transparent organisms detection. The improved contents include: adding the adaptive spatial feature fusion module for weighted fusion, making full use of features between different scales; the Focal loss function is used to alleviate the imbalance of categories in the dataset; using α-iou function to perform more accurate boundary box regression to improve the accuracy of location. The experimental results show that compared with the traditional YOLOX algorithm, the mAP of the algorithm proposed in this paper is increased by 2.58%, and it is also greatly improved compared with Fast R-CNN, SSD, and other algorithms, which proves the effectiveness and superiority of the improved YOLOX algorithm in this paper.}
}