Improved YOLOv7 Algorithm for Small Object Detection in Unmanned Aerial Vehicle Image Scenarios

Abstract

Object detection in unmanned aerial vehicle (UAV) images has become a popular research topic in recent years. However, UAV images are captured from high altitudes with a large proportion of small objects and dense object regions, posing a significant challenge to small object detection. To solve this issue, we propose an efficient YOLOv7-UAV algorithm in which a low-level prediction head (P2) is added to detect small objects from the shallow feature map, and a deep-level prediction head (P5) is removed to reduce the effect of excessive down-sampling. Furthermore, we modify the bidirectional feature pyramid network (BiFPN) structure with a weighted cross-level connection to enhance the fusion effectiveness of multi-scale feature maps in UAV images. To mitigate the mismatch between the prediction box and ground-truth box, the SCYLLA-IoU (SIoU) function is employed in the regression loss to accelerate the training convergence process. Moreover, the proposed YOLOv7-UAV algorithm has been quantified and compiled in the Vitis-AI development environment and validated in terms of power consumption and hardware resources on the FPGA platform. The experiments show that the resource consumption of YOLOv7-UAV is reduced by 28%, the mAP is improved by 3.9% compared to YOLOv7, and the FPGA implementation improves the energy efficiency by 12 times compared to the GPU.

Improved YOLOv7 Target Detection Algorithm Based on UAV Aerial Photography

“…The experiment illustrates the effectiveness and practicality of this algorithm for detecting weak targets in UAV aerial images. Additionally, in order to reflect the advancement of this algorithm and to compare it with the current technical level of the YOLO V7 algorithm in the field of UAV, YOLOv7-UAV [20], PDWT-YOLO [21], and improved YOLOv7 algorithms are selected to compare with this algorithm [22]. It can be seen from Table 4 that this thesis shows that the YOLOv7-UAV algorithm is superior to this algorithm in terms of the parameters, but this algorithm in terms of this index is superior to the PDWT-YOLO and improved YOLOv7 algorithm.…”

A Multi-Stage Approach to UAV Detection, Identification, and Tracking Using Region-of-Interest Management and Rate-Adaptive Video Coding

“…These modules and optimization methods are called trainable bag-of-freebies; they improve detection accuracy without increasing the inference cost. Additionally, it is already proven that the YOLOv7 can detect small flying objects such as UAVs [19]. Due to the nature of the UAV surveillance setting-capturing images of flying UAVs from a distance-and the fast maneuverability of UAVs capable of flying 60 km/h, quickly determining the bounding boxes accurately makes YOLOv7 a suitable choice for the proposed system.…”