# EXOLOv3: An Efficient Real-Time Detection Model for Floating Object on River
## 2 Related Works
### Deep-Learning-Based Methods  
In 2006, [PERSON], published a paper in Science, and proposed the idea of Deep Belief Networks [22] for the first time, which opened the prelude to the study of deep learning. In 2012, [PERSON] proposed AlexNet [23], which won the first place in ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). This fully demonstrates the effectiveness of deep learning in image recognition tasks and its great potential in image processing.  
With the development of deep learning, the performance of object detection is also improving. R-CNN was proposed in 2014 as a pioneering work in the field of object detection, which improved the accuracy of object detection greatly by combining the good automatic feature extraction and classification prediction of convolution neural network (CNN) with selective search. Subsequent work has improved this method too. In 2015, [PERSON] proposed Fast R-CNN [24], which use the idea of SPP-Net [25] to propose ROI pooling layer, and a multitask loss function to incorporate both classification prediction and bounding box prediction, thus greatly improving the accuracy and speed of object detection. However, as Fast R-CNN uses a selective search strategy for region suggestions, the time required to generate candidate regions rises. [PERSON] proposed the Faster R-CNN network [26], which designs a new Region Proposal Network (RPN), which hands over the task of region proposals to convolution network and improves the detection accuracy and speed. Since small objects have always been the difficulty of object detection, [PERSON] et al. [27] proposed a Faster R-CNN algorithm with Class Activation for the detection and localization of floating objects on the water surface. From the results, the algorithm has strong adaptability when detecting small objects. However, it does not combine the statistical characteristics of floating objects in the region for further analysis. In the instance segmentation, the large receptive field pays more attention to the detail information, while the small receptive field pays more attention to the semantic information. [PERSON] et al. [28] proposed a Mask refined R-CNN network based on the object detection network Mask R-CNN. By constructing a feature pyramid network, the network can sum the forward and backward transmission of feature maps with the same resolution to achieve feature fusion, so as to balance the network performance. The above methods belong to two-stage object detectors and are less real-time than one-stage methods. In 2016, the end-to-end phase of YOLO [29] was proposed, abandoning the region proposals phase, so that object detection can achieve real-time detection speed, but detection accuracy is lower. In view of the problems of YOLO, YOLOv2 [30] and YOLOv3 [31] are proposed subsequently.  
YOLOv3 has higher detection accuracy and detection speed, but is still not effective for floating objects on river which including small objects, and having dense object distribution and diverse shapes. [PERSON] et al. [32] proposed a coordinated attention mechanism into the YOLOv5 model, which enabled the model to extract features quickly and efficiently, and improved the recall rate, average accuracy and F1 score on both public and private datasets. However, it also brings higher computational complexity and longer training time. Due to the real-time requirement, [PERSON] et al. [33] proposed an improved YOLO for floating debris in waterway based on a one-stage algorithm. As the floating objects on river in China are different from the floating debris in waterway, we propose our method for the detection of floating objects on river, from the creation of the dataset to the design of the model, and to the end-to-end real-time floating detection in the video stream.