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Nowadays, many RGB-D saliency detection models have been proposed, and they are conducted based on the pixel-level depth data obtained by active depth sensors or dense stereo matching algorithms in advance. However, the pixel-level depth maps may have a negative impact on saliency ranking, especially when they are inconsistent or invalid within the object area. It can be found that people tend to observe an object as a whole and ignore the details of the target surface. Inspired by this characteristic, we propose a salient object detection method via joint perception of region-level spatial distribution and color contrast. First, a twice segmentation strategy based on multifeature fusion is used to compute the region-level information. Then, the region-level spatial distribution maps are constructed instead of pixel-level depth maps, which is helpful for avoiding the interference of dense depth information. To improve the integrity of object detection, color saliency maps are also computed based on regional segmentation information. After that, we adopt a fusion strategy to achieve the effective complementarity of the two kinds of information. Two optimization strategies are employed to further improve the results of saliency ranking. Experimental results on two benchmark datasets demonstrate that the proposed method has better performance than most of the state-of-the-art methods, and it also shows competitive capability compared with deep learning-based methods.

In order to clean the oxide layer and contaminations on the surface of piezoelectric ceramic sheets and solder rings, an automatic wiping system based on the cooperative robot UR3 is designed. The wiping system takes UR3 robot as the moving unit, and employs the micro force sensor to detect the wiping force. The end effector of the robot is designed according to the size and shape of the parts. Vacuum adsorption stages for fixed piezoelectric components were designed, in which silicon wafer etched by MEMS process was served as the adsorption head for fixing solder rings. The vacuum generator is selected according to the stress of piezo components. Finally, the feasibility of the adsorption device was proved by experiments, and the actual vacuum adsorption force under different exhaust pressures was measured. The device completes the surface cleaning of the piezoelectric ceramic sheets and the solder rings, thereby improving the reliability of subsequent soldering.

Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV’s real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering.