Multi-level refined UAV image target detection

In UAV (unmanned aerial vehicle) image target detection, challenges such as complex backgrounds, variable lighting conditions, target occlusion, and scale variations often lead to missed and false detections. To address these challenges and enhance both detection accuracy and robustness, we propose a multi-level fine-grained object detection algorithm for UAV images. This algorithm integrates several novel components and mechanisms aimed at improving feature representation across different scales, suppressing background noise, and accelerating model convergence, thus significantly optimizing detection performance. First, we introduce a cross-stage partial selective multi-scale feature fusion (CSP-SMSFF) module. This module combines multi-scale feature extraction and feature fusion enhancement strategies. By incrementally increasing convolution kernels and channel fusion, it accurately captures multi-scale target features. The CSP-SMSFF module effectively enhances the algorithm’s ability to handle targets of various sizes, which improves detection accuracy across different target scales, ensuring that small and large targets are both detected with high precision. Next, we incorporate the adaptive fine-grained channel attention (AFGCAttention) mechanism. This mechanism optimizes channel feature representations via dynamic tuning, improving the algorithm’s ability to perceive and discriminate important multi-scale sample features. It is particularly effective in handling complex backgrounds and occlusions, where the ability to retain fine-grained mapping information while suppressing background noise is crucial. This dynamic adjustment significantly reduces missed detections, especially for small and edge targets, thereby improving overall detection accuracy. Subsequently, we design the shared group convolution efficient (SGCE-Head) detection head, which utilizes efficient multi-scale convolution (EMSPConv) to capture both global important features and local fine details in the spatial-channel dimension. By improving the ability to locate and recognize multi-scale features, SGCE-Head addresses false detection issues and ensures that the algorithm distinguishes targets from backgrounds effectively, leading to more accurate and reliable results. Finally, we propose the Inner-Powerful-IoUv2 loss function. This function optimizes medium-quality anchor boxes and balances gradient strength for high and low IoU (intersection over union) samples. It enhances target localization accuracy and increases region attention, accelerating model convergence. The use of this loss function allows the model to achieve higher detection accuracy in a shorter amount of time, improving both its practical utility and efficiency. Experimental results on the VisDrone2019 and VisDrone2021 datasets demonstrate the effectiveness of our proposed method. Specifically, the algorithm achieves mAP@0.5 values of 47.5% and 45.3%, representing improvements of 5.7% and 4.7% over the baseline model. These results show that our algorithm not only outperforms existing state-of-the-art methods in terms of detection accuracy but also offers good versatility and real-time performance, making it suitable for a variety of application scenarios. In conclusion, the algorithm presented in this paper, by innovatively combining the CSP-SMSFF module, AFGCAttention mechanism, SGCE-Head detection head, and Inner-Powerful-IoUv2 loss function, provides a comprehensive solution to the core challenges of UAV image target detection. It significantly enhances detection accuracy, robustness, and real-time performance. This approach effectively addresses issues such as complex backgrounds, lighting variations, occlusion, and scale changes, while demonstrating strong versatility and achieving impressive results in practical applications.