Aircraft surface inspection includes detecting surface defects caused by corrosion and cracks and stains from the oil spill, grease, dirt sediments, etc. In the conventional aircraft surface inspection process, human visual inspection is performed which is time-consuming and inefficient whereas robots with onboard vision systems can inspect the aircraft skin safely, quickly, and accurately. This work proposes an aircraft surface defect and stain detection model using a reconfigurable climbing robot and an enhanced deep learning algorithm. A reconfigurable, teleoperated robot, named as “Kiropter,” is designed to capture the aircraft surface images with an onboard RGB camera. An enhanced SSD MobileNet framework is proposed for stain and defect detection from these images. A Self-filtering-based periodic pattern detection filter has been included in the SSD MobileNet deep learning framework to achieve the enhanced detection of the stains and defects on the aircraft skin images. The model has been tested with real aircraft surface images acquired from a Boeing 737 and a compact aircraft’s surface using the teleoperated robot. The experimental results prove that the enhanced SSD MobileNet framework achieves improved detection accuracy of aircraft surface defects and stains as compared to the conventional models.

This paper proposes a visual dirt detection algorithm and a novel adaptive tiling-based selective dirt area coverage scheme for reconfigurable morphology robot. The visual dirt detection technique utilizes a three-layer filtering framework which includes a periodic pattern detection filter, edge detection, and noise filtering to effectively detect and segment out the dirt area from the complex floor backgrounds. Then adaptive tiling-based area coverage scheme has been employed to generate the tetromino morphology to cover the segmented dirt area. The proposed algorithms have been validated in Matlab environment with real captured dirt images and simulated tetrominoes tile set. Experimental results show that the proposed three-stage filtering significantly enhances the dirt detection ratio in the incoming images with complex floors with different backgrounds. Further, the selective dirt area coverage is performed by excluding the already cleaned area from the unclean area on the floor map by incorporating the tiling pattern generated by adaptive tetromino tiling algorithm.

One of the components of the advanced technologies has been characterized by some of the devices like the wireless in-vivo actuators and sensors. Besides, the in-vivo wireless medical devices, along with their related technologies, are representing the nextstage of such development and offering scalable and inexpensive solutions and wearable devices integration. Reducing the surgeries' invasiveness and offering nonstop health monitoring are provided via in-vivo WBAN devices. Also, the information of patients might be obtained over a large time period; also, physicians have the capability for performing highly-dependable analysis via using the concept of big data compared to depending on the recorded data in short hospital visits. Similarly, taken into account the huge fading regarding in-vivo channels due to the signal path passing through flesh, bones, skins, and blood guaranteeing that the received data is the same as the sent one, channel coding is considered as a solution for increasing the effectiveness and overcoming the wireless links suffering from Inter Symbol Interference (ISI). Besides, all simulations have been utilized with the use of 50 MHz bandwidth at Ultra-Wideband frequencies (3.10-10.60GHz). In the presented study, the data transmission performance over the in-vivo channel is improved by using optimal channel coding. In addition, the results show that the bit error rate performance associated with turbo codes provided significant improvement via enhancing BER and outperforming the polar and convolutional codes while it is used for data. Apart from convolutional code, other methods are performing close to each other, which becomes true when the information block length becomes large. The simulation in this study indicates that because of the dense structure regarding the human body, in-vivo channel provides less performance compared to the Rayleigh channel due to the path by which the signal is coming (Flesh, Skins, Blood, Bones, Muscles, and Fat).