Accurate and efficient object detection in UAV images is a challenging task due to the diversity of target scales and the massive number of small targets. This study investigates the enhancement in the detection head using sparse convolution, demonstrating its effectiveness in achieving an optimal balance between accuracy and efficiency. Nevertheless, the sparse convolution method encounters challenges related to the inadequate incorporation of global contextual information and exhibits network inflexibility attributable to its fixed mask ratios. To address the above issues, the MFFCESSC-SSD, a novel single-shot detector (SSD) with multi-scale feature fusion and context-enhanced spatial sparse convolution, is proposed in this paper. First, a global context-enhanced group normalization (CE-GN) layer is developed to address the issue of information loss resulting from the convolution process applied exclusively to the masked region. Subsequently, a dynamic masking strategy is designed to determine the optimal mask ratios, thereby ensuring compact foreground coverage that enhances both accuracy and efficiency. Experiments on two datasets (i.e., VisDrone and ARH2000; the latter dataset was created by the researchers) demonstrate that the MFFCESSC-SSD remarkably outperforms the performance of the SSD and numerous conventional object detection algorithms in terms of accuracy and efficiency.

There are many small objects in UAV images, and the object scale varies greatly. When the SSD algorithm detects them, the backbone network’s feature extraction capabilities are poor; it does not fully utilize the semantic information in the deeper feature layer, and it does not give enough consideration to the little items in the loss function, which result in serious missing object detection and low object detection accuracy. To tackle these issues, a new algorithm called RSAD (Resnet Self-Attention Detector) that takes advantage of the self-attention mechanism has been proposed. The proposed RSAD algorithm utilises the residual structure of the ResNet-50 backbone network, which is more capable of feature extraction, in order to extract deeper features from UAV image information. It then utilises the SAFM (Self-Attention Fusion Module) to reshape and concatenate the shallow and deep features of the backbone network, selectively weighted by attention units, ensuring the efficient fusion of features to provide rich semantic features for small object detection. Lastly, it introduces the Focal Loss loss function, which adjusts the corresponding parameters to enhance the contribution of small objects to the detection model. The ablation experiments show that the mAP of RSAD is 10.6% higher than that of the SSD model, with SAFM providing the highest mAP enhancement of 7.4% and ResNet-50 and Focal Loss providing 1.3% and 1.9% enhancements, respectively. The detection speed is only reduced by 3FPS, but it meets the real-time requirement. Comparison experiments show that in terms of mAP, it is far ahead of Faster R-CNN, Cascade R-CNN, RetinaNet, CenterNet, YOLOv5s, and YOLOv8n, which are the mainstream object detection models; In terms of FPS, it slightly inferior to YOLOv5s and YOLOv8n. Thus, RSAD has a good balance between detection speed and accuracy, and it can facilitate the advancement of the UAV to complete object detection tasks in different scenarios.