# A Novel FFT_YOLOX Model for Underwater Precious Marine Product Detection
## 1 Introduction  
In recent years, precious marine product, including sea cucumbers, sea urchin and scallop, etc., have been widely noticed due to their use as food and folk medicines in communities across the world. The aquaculture of these products is growing rapidly with high economic benefits. Therefore, the fishing and culture of precious marine products are becoming much more significant. However, these fishing activities are mainly dependent on manual work, which is labor-intensive, high-cost and time-consuming. Furthermore, long-term underwater work is extremely threatening to human beings. With the rapid growth of underwater robot technology, automated fishing of sea cucumbers, sea urchin and scallop is becoming increasingly popular. An underwater robot can be used to monitor the size of marine products, and fish the mature ones automatically with high efficiency, labor saving. Automatic detection of marine products from underwater images is one of the most important steps in developing an underwater robot perceiving method of previous marine products for fishing in the real world. In the literature, several image-processing methods for precious products, such as sea cucumbers, have been developed. Some specific featuresof underwater objects have been considered and used for their detection. Separating the region of interest from the background based on edge features are proposed by [1; 2; 3]. These methods can achieve a good performance base on an appropriate threshold value of gray levels of objects and background. To acquire better results, further features are considered in detection methods [4; 5; 6], including shape, color, intensity and texture. In these traditional image processing approaches, the features used to recognize marine objects are hand-crafted, and the accuracy of detection is deeply related to the effectiveness of key feature extraction. In recent years, deep learning (DL) has become increasingly popular. Convolutional neural networks (CNN) are considered to be the most representative DL method. CNNs have been widely used in a series of computer vision tasks, such as image classification [7; 8; 9; 10; 11], object detection [12; 13; 14], and semantic segmentation [15; 16; 17; 18]. For object detection, several popular methods have been proposed, including two-stage and one-stage frameworks. A two-stage framework firstly carries out a region with a high probability of interest objects, then the proposal regions are used as the inputs of CNN for object recognition. The classical two-stage methods, e.g., R-CNN [19], R-FCN [20], Fast RCNN [21], and Faster RCNN [14], can obtain high accuracy, but suffer from complicated computation and slow inference time. A one-stage framework object detection method can address the previously proposed issues. The YOLO family [22; 23; 24; 25; 26] methods are the most popular one-stage detection methods, whilst several other underwater detection algorithms have also been proposed. For instance, [PERSON] et al. [27] proposed a modified YOLO v3 model for fish detection, which changed the original backbone into MobileNet [28; 29; 30] v1 to decrease the parameters of its detection model and accelerate inference process. In [31], an underwater fish is detected online using a YOLO-based model. Recently, the YOLOX [26] is one of the most popular YOLO-series methods. It combines the region proposal process with object detection and proposes an anchor-free detection head for increased efficiency and reduced computation cost. Therefore, in this study we used the YOLOX as our basic detection framework. In previous detection models, the backbone, which is used for feature extraction, is mainly reliant on CNNs, such as ResNets [32] and darknet [22; 23; 24; 25]. In CNN architecture, the discrete convolutional filters (e.g., 3 \(\times\) 3, 5 \(\times\) 5 or 7 \(\times\) 7 kernels) are used to capture spatial information of an image locally. Due to the small kernel size, it suffers from a limited receptive field which hinders the modeling of long-range dependencies.