An enhanced DEIM-based method for target detection in complex field environments
To address the issues of complex background interference, similar target misdetection and low recognition accuracy of tiny objects in Unmanned Aerial Vehicle (UAV) object detection scenarios, this study proposes an enhanced DEIM-based method for target detection in complex field environments. First, the backbone network is integrated with an Omni-Dimensional Dynamic Convolution (ODConv) module, which alleviates false detection caused by the indistinct feature differences between similar distractors and objects by exploiting the multi-dimensional dynamic attention learning mechanism in the convolution kernel space. Second, a weighted convolution-based C3k2 (wConv-C3k2) module is constructed in the encoder network, which suppresses redundant background information and reduces missed detection caused by background noise by introducing a spatial density function to dynamically adjust the weights of the convolution kernel. Finally, a Deep Robust Feature Downsampling (DRFD) module is designed, which reduces the loss of key features and enhances the capability of small object detection by fusing three branches with complementary characteristics. Experimental results demonstrate that compared with the baseline DEIM model, the proposed model yields 3.5%, 4.9%, and 3.0% improvements in AP50:95, AP75, and AP50, respectively, together with a 5.4% increase in mAR. This method effectively improves the accuracy and reliability of target detection in aerial images, enriches the technical approaches of intelligent control and information perception, and provides reliable technical support for the practical applications of information and control systems in field rescue.