| Summary: | Detecting objects from images captured by Unmanned Aerial Vehicles (UAVs) is a highly demanding task. It is also considered a very challenging task due to the typically cluttered background and diverse dimensions of the foreground targets, especially small object areas that contain only very limited information. Multi-scale representation learning presents a remarkable approach to recognizing small objects. However, this strategy ignores the combination of the sub-parts in an object and also suffers from the background interference in the feature fusion process. To this end, we propose a <b>Fi</b>ne-grained Target <b>Fo</b>cusing <b>Net</b>work (FiFoNet) which can effectively select a combination of multi-scale features for an object and block background interference, which further revitalizes the differentiability of the multi-scale feature representation. Furthermore, we propose a Global–Local Context Collector (GLCC) to extract global and local contextual information and enhance low-quality representations of small objects. We evaluate the performance of the proposed FiFoNet on the challenging task of object detection in UAV images. A comparison of the experiment results on three datasets, namely VisDrone2019, UAVDT, and our VisDrone_Foggy, demonstrates the effectiveness of FiFoNet, which outperforms the ten baseline and state-of-the-art models with remarkable performance improvements. When deployed on an edge device NVIDIA JETSON XAVIER NX, our FiFoNet only takes about 80 milliseconds to process an drone-captured image.
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