Unmanned Aerial Vehicle Perspective Small Target Recognition Algorithm Based on Improved YOLOv5

Abstract

Small target detection has been widely used in applications that are relevant to everyday life and have many real-time requirements, such as road patrols and security surveillance. Although object detection methods based on deep learning have achieved great success in recent years, they are not effective in small target detection. In order to solve the problem of low recognition rate caused by factors such as low resolution of UAV viewpoint images and little valid information, this paper proposes an improved algorithm based on the YOLOv5s model, called YOLOv5s-pp. First, to better suppress interference from complex backgrounds and negative samples in images, we add a CA attention module, which can better focus on task-specific important channels while weakening the influence of irrelevant channels. Secondly, we improve the forward propagation and generalisation of the network using the Meta-ACON activation function, which adaptively learns to adjust the degree of linearity or nonlinearity of the activation function based on the input data. Again, the SPD Conv module is incorporated into the network model to address the problems of reduced learning efficiency and loss of fine-grained information due to cross-layer convolution in the model. Finally, the detection head is improved by using smaller, smaller-target detection heads to reduce missed detections. We evaluated the algorithm on the VisDrone2019-DET and UAVDT datasets and compared it with other state-of-the-art algorithms. Compared to YOLOv5s, mAP@.5 improved by 7.4% and 6.5% on the VisDrone2019-DET and UAVDT datasets, respectively, and compared to YOLOv8s, mAP@.5 improved by 0.8% and 2.1%, respectively. For improving the performance of the UAV-side small target detection algorithm, it will help to enhance the reliability and safety of UAVs in critical missions such as military reconnaissance, road patrol and security surveillance.

APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion

“…The results show that this method can overcome the interference of sea clutter, improving the detection accuracy of small targets on the sea surface. To better suppress the interference of complex backgrounds and negative samples in images, Xu et al [ 24 ] proposed a YOLOv5s-pp algorithm, which introduces the convolutional block attention module (CBAM) to improve the feature extraction ability for small targets. The attention mechanism enables the model to autonomously learn and weight different input features by calculating the correlation between them, making the model more sensitive to important regions in the image.…”

Detector Consistency Research on Remote Sensing Object Detection

“…Remote sensing object detection is the process of determining the location and the category of objects in optical remote sensing images. In recent years, a large number of remote sensing object detection methods [1,2] have been proposed based on the deep learning technique. As an important and practical research field, the object detection task is not only used in the detection of ships [3], airports [4], vehicles [5] and other objects, but is also widely used in object tracking [6], instance segmentation [7], caption generation [8] and many other fields.…”