Reveal of Domain Effect: How Visual Restoration Contributes to Object Detection in Aquatic Scenes

Underwater robotic perception usually requires visual restoration and object detection, both of which have been studied for many years. Meanwhile, data domain has a huge impact on modern data-driven leaning process. However, exactly indicating domain effect, the relation between restoration and detection remains unclear. In this paper, we generally investigate the relation of quality-diverse data domain to detection performance. In the meantime, we unveil how visual restoration contributes to object detection in real-world underwater scenes. According to our analysis, five key discoveries are reported: 1) Domain quality has an ignorable effect on within-domain convolutional representation and detection accuracy; 2) low-quality domain leads to higher generalization ability in cross-domain detection; 3) low-quality domain can hardly be well learned in a domain-mixed learning process; 4) degrading recall efficiency, restoration cannot improve within-domain detection accuracy; 5) visual restoration is beneficial to detection in the wild by reducing the domain shift between training data and real-world scenes. Finally, as an illustrative example, we successfully perform underwater object detection with an aquatic robot.

A Multimodal, Enveloping Soft Gripper: Shape Conformation, Bioinspired Adhesion, and Expansion-Driven Suction

A key challenge in robotics is to create efficient methods for grasping objects with diverse shapes, sizes, poses, and properties. Grasping with hand-like end effectors often requires careful selection of hand orientation and finger placement. Here, we present a soft, fingerless gripper capable of efficiently generating multiple grasping modes. It is based on a soft, cylindrical accordion structure containing coupled, parallel fluidic channels. It is controlled via pressure supplied from a single fluidic port. Inflation opens the gripper orifice for enveloping an object, while deflation allows it to produce grasping forces. The interior is patterned with a gecko-like skin that increases friction, enabling the gripper to lift objects weighing up to 20 N. Our design ensures that fragile objects, such as eggs, can be safely handled, by virtue of a wall buckling mechanism. The gripper can integrate a lip that enables it to form a seal and, upon inflating, to generate suction for lifting objects with flat surfaces. The gripper may also be inflated to expand into an opening or orifice for grasping objects with handles or openings. We describe the design and fabrication of this device and present an analytical model of its behavior when operated from a single fluidic port. In experiments, we demonstrate its ability to grasp diverse objects, and show that its performance is well described by our model. Our findings show how a fingerless soft gripper can efficiently perform a variety of grasping operations. Such devices could improve the ability of robotic systems to meet applications in areas of great economic and societal importance.

Towards Real-Time Accurate Object Detection in Both Images and Videos Based on Dual Refinement

Object detection has been vigorously studied for years but fast accurate detection for real-world applications remains a very challenging problem: i) Most existing methods have either high accuracy or fast speed; ii) Most prior-art approaches focus on static images, ignoring temporal information in real-world scenes. Overcoming drawbacks of single-stage detectors, we take aim at precisely detecting objects in both images and videos in real time. Firstly, as a dual refinement mechanism, a novel anchor-offset detection including an anchor refinement, a feature offset refinement, and a deformable detection head is designed for two-step regression and capturing accurate detection features. Based on the anchor-offset detection, a dual refinement network (DRN) is developed for high-performance static detection, where a multi-deformable head is further designed to leverage contextual information for describing objects. As for video detection, temporal refinement networks (TRN) and temporal dual refinement networks (TDRN) are developed by propagating the refinement information across time. Our proposed methods are evaluated on PASCAL VOC, COCO, and ImageNet VID datasets. Extensive comparison on static and temporal detection validate the superiority of the DRN, TRN and TDRN. Consequently, our developed approaches achieve a significantly enhanced detection accuracy and make prominent progress in accuracy vs. speed trade-off. Codes will be publicly available.

Towards Quality Advancement of Underwater Machine Vision with Generative Adversarial Networks

Underwater machine vision has attracted significant attention, but its low quality has prevented it from a wide range of applications. Although many different algorithms have been developed to solve this problem, real-time adaptive methods are frequently deficient. In this paper, based on filtering and the use of generative adversarial networks (GANs), two approaches are proposed for the aforementioned issue, i.e., a filtering-based restoration scheme (FRS) and a GAN-based restoration scheme (GAN-RS). Distinct from previous methods, FRS restores underwater images in the Fourier domain, which is composed of a parameter search, filtering, and enhancement. Aiming to further improve the image quality, GAN-RS can adaptively restore underwater machine vision in real time without the need for pretreatment. In particular, information in the Lab color space and the dark channel is developed as loss functions, namely, underwater index loss and dark channel prior loss, respectively. More specifically, learning from the underwater index, the discriminator is equipped with a carefully crafted underwater branch to predict the underwater probability of an image. A multi-stage loss strategy is then developed to guarantee the effective training of GANs. Through extensive comparisons on the image quality and applications, the superiority of the proposed approaches is confirmed. Consequently, the GAN-RS is considerably faster and achieves a state-of-the-art performance in terms of the color correction, contrast stretch, dehazing, and feature restoration of various underwater scenes. The source code will be made available.