Real-time object tracking on a drone under a dynamic environment has been a challenging issue for many years, with existing approaches using off-line calculation or powerful computation units on board. This paper presents a new lightweight real-time onboard object tracking approach with multi-inertial sensing data, wherein a highly energy-efficient drone is built based on the Snapdragon flight board of Qualcomm. The flight board uses a digital signal processor core of the Snapdragon 801 processor to realize PX4 autopilot, an open-source autopilot system oriented toward inexpensive autonomous aircraft. It also uses an ARM core to realize Linux, robot operating systems, open-source computer vision library, and related algorithms. A lightweight moving object detection algorithm is proposed that extracts feature points in the video frame using the oriented FAST and rotated binary robust independent elementary features algorithm and adapts a local difference binary algorithm to construct the image binary descriptors. The K-nearest neighbor method is then used to match the image descriptors. Finally, an object tracking method is proposed that fuses inertial measurement unit data, global positioning system data, and the moving object detection results to calculate the relative position between coordinate systems of the object and the drone. All the algorithms are run on the Qualcomm platform in real time. Experimental results demonstrate the superior performance of our method over the state-of-the-art visual tracking method.

Recent studies employ advanced deep convolutional neural networks (CNNs) for monocular depth perception, which can hardly run efficiently on small drones that rely on low/middle-grade GPU(e.g. TX2 and 1050Ti) for computation. In addition, the methods which can effectively and efficiently produce probabilistic depth prediction with a measure of model confidence have not been well studied. The lack of such a method could yield erroneous, sometimes fatal, decisions in drone applications (e.g. selecting a waypoint in a region with a large depth yet a low estimation confidence). This paper presents a real-time onboard approach for monocular depth prediction and obstacle avoidance with a lightweight probabilistic CNN (pCNN), which will be ideal for use in a lightweight energy-efficient drone. For each video frame, our pCNN can efficiently predict its depth map and the corresponding confidence. The accuracy of our lightweight pCNN is greatly boosted by integrating sparse depth estimation from a visual odometry into the network for guiding dense depth and confidence inference. The estimated depth map is transformed into Ego Dynamic Space (EDS) by embedding both dynamic motion constraints of a drone and the confidence values into the spatial depth map. Traversable waypoints are automatically computed in EDS based on which appropriate control inputs for the drone are produced. Extensive experimental results on public datasets demonstrate that our depth prediction method runs at 12Hz and 45Hz on TX2 and 1050Ti GPU respectively, which is 1.8X~5.6X faster than the state-of-the-art methods and achieves better depth estimation accuracy. We also conducted experiments of obstacle avoidance in both simulated and real environments to demonstrate the superiority of our method to the baseline methods.

3-D imaging sonar is crucial in the exploration of marine resources, and the development of portable device with high-imaging quality and high-real-time performance is the general trend. However, traditional framework methods are limited by the huge amount of computation brought by high-quality imaging, making it difficult to implement in engineering. To address this issue, we develop 3-D real-time sonar system in an algorithm-hardware co-designed way. An ultrawideband distributed and parallel subarray beamforming algorithm (UWB-DPS) is proposed for 3-D acoustic imaging. This is a multistage array time-frequency beamforming method under a distributed parallel computing architecture. Based on this, we propose field-programmable gate array (FPGA)-based accelerator. It divides a large sonar receiving planar array into multiple parallel subarrays, and complete the beamforming in two stages, which can reduce the calculation load and speeds up 3-D imaging. For engineering implementation, we optimized the sparseness of the planar transducer array, with a sparse rate as high as 97.7%. The experimental results show that the calculation amount of the proposed UWB-DPS algorithm is reduced to 1/5.7 of the traditional framework algorithm, the imaging performance is effectively improved, and the FPGA-based accelerator outperforms the CPU software implementation by 935 $\times $ .

In current multimodal sentiment analysis, aligned and complete multimodal sequences are often crucial. Obtaining complete multimodal data in the real world presents various challenges, and aligning multimodal sequences often requires a significant amount of effort. Unfortunately, most multimodal sentiment analysis methods fail when dealing with missing modalities or unaligned multimodal sequences. To tackle these two challenges simultaneously in a simple and lightweight manner, we present the Unified Multimodal Framework (UniMF). The primary components of UniMF comprise two distinct modules. The first module, Translation Module, translates missing modalities using information from existing modalities. The second module, Prediction Module, uses the attention mechanism to fuse the multimodal information and generate predictions. To enhance the translation performance of the Translation Module, we introduce the Multimodal Generation Mask (MGM) and utilize it to construct the Multimodal Generation Transformer (MGT). The MGT can generate the missing modality while focusing on information from existing modalities. Furthermore, we introduce the Multimodal Understanding Transformer (MUT) in the Prediction Module, which includes the Multimodal Understanding Mask (MUM) and a unique sequence, MultiModalSequence ( MMSeq ), representing a unified multimodality. To assess the performance of UniMF, we perform experiments on four multimodal sentiment datasets, and UniMF attains competitive or state-of-the-art outcomes with fewer learnable parameters. Furthermore, the experimental outcomes signify that UniMF, supported by MGT and MUT - two transformers utilizing special attention mechanisms, can efficiently manage both generating task of missing modalities and understanding task of unaligned multimodal sequences. The implementation of this work is publicly accessible at https://github.com/gw-zhong/UniMF .

Camouflaged Object Detection (COD) aims to identify and segment objects that blend into their surroundings. Since the color and texture of the camouflaged objects are extremely similar to the surrounding environment, it is super challenging for vision models to precisely detect them. Inspired by research on biology and evolution, we introduce depth information as an additional cue to help break camouflage, which can provide spatial information and texture-free separation for foreground and background. To dig clues of camouflaged objects in both RGB and depth modalities, we innovatively propose Depth-aided Camouflaged Object Detection (DaCOD), which involves two key components. We firstly propose the Multi-modal Collaborative Learning (MCL) module, which aims to collaboratively learning deep features from both RGB and depth channels via a hybrid backbone. Then, we propose a novel Cross-modal Asymmetric Fusion (CAF) strategy, which asymmetrically fuse RGB and depth information for complementary depth feature enhancement to produce accurate predictions. We conducted numerous experiments of the proposed DaCOD on three widely-used challenging COD benchmark datasets, in which DaCOD outperforms the current state-of-the-arts by a large margin. All resources are available at https://github.com/qingwei-wang/DaCOD.