Object detection is essential to the interpretation of optical remote sensing images and can serve as a foundation for research into additional visual tasks that utilize remote sensing. However, the object detection network currently employed in optical remote sensing images underutilizes the output of the feature pyramid, so there remains potential for an improved detection. At present, a suitable balance between the detection efficiency and detection effect is difficult to attain. This paper proposes an enhanced YOLOv5 algorithm for object detection in high-resolution optical remote sensing images, utilizing multiple layers of the feature pyramid, a multi-detection-head strategy, and a hybrid attention module to improve the effect of object-detection networks for use with optical remote sensing images. According to the SIMD dataset, the mAP of the proposed method was 2.2% better than YOLOv5 and 8.48% better than YOLOX, achieving an improved balance between the detection effect and speed.

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<abstract><p>Object detection in drone-captured scenarios is a recent popular task. Due to the high flight altitude of unmanned aerial vehicle (UAV), the large variation of target scales, and the existence of dense occlusion of targets, in addition to the high requirements for real-time detection. To solve the above problems, we propose a real-time UAV small target detection algorithm based on improved ASFF-YOLOv5s. Based on the original YOLOv5s algorithm, the new shallow feature map is passed into the feature fusion network through multi-scale feature fusion to improve the extraction capability for small target features, and the Adaptively Spatial Feature Fusion (ASFF) is improved to improve the multi-scale information fusion capability. To obtain anchor frames for the VisDrone2021 dataset, we improve the K-means algorithm to obtain four different scales of anchor frames on each prediction layer. The Convolutional Block Attention Module (CBAM) is added in front of the backbone network and each prediction network layer to improve the capture capability of important features and suppress redundant features. Finally, to address the shortcomings of the original GIoU loss function, the SIoU loss function is used to accelerate the convergence of the model and improve accuracy. Extensive experiments conducted on the dataset VisDrone2021 show that the proposed model can detect a wide range of small targets in various challenging environments. At a detection rate of 70.4 FPS, the proposed model obtained a precision value of 32.55%, F1-score of 39.62%, and a mAP value of 38.03%, which improved 2.77, 3.98, and 5.1%, respectively, compared with the original algorithm, for the detection performance of small targets and to meet the task of real-time detection of UAV aerial images. The current work provides an effective method for real-time detection of small targets in UAV aerial photography in complex scenes, and can be extended to detect pedestrians, cars, etc. in urban security surveillance.</p></abstract>

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