Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices

Internet of Things(IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power. However, to provide time-critical services such as emergency response, home assistance, surveillance, etc, these devices often need real-time analysis of their camera data. This paper strives to offer a viable approach to integrate high-performance deep learning-based computer vision algorithms with low-resource and low-power devices by leveraging the computing power of the cloud. By offloading the computation work to the cloud, no dedicated hardware is needed to enable deep neural networks on existing low computing power devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the power of using cloud computing to perform real-time vision tasks. Furthermore, to reduce latency and improve real-time performance, compression algorithms are proposed and evaluated for streaming real-time video frames to the cloud.Comment: Accepted to The 11th International Conference on Machine Vision (ICMV 2018). Project site: https://zhengyiluo.github.io/projects/cloudchaser

Impacting device for testing insulation

An electro-mechanical impacting device for testing the bonding of foam insulation to metal is descirbed. The device lightly impacts foam insulation attached to metal to determine whether the insulation is properly bonded to the metal and to determine the quality of the bond. A force measuring device, preferably a load cell mounted on the impacting device, measures the force of the impact and the duration of the time the hammer head is actually in contact with the insulation. The impactor is designed in the form of a handgun having a driving spring which can propel a plunger forward to cause a hammer head to impact the insulation. The device utilizes a trigger mechanism which provides precise adjustements, allowing fireproof operation

Real-Time Grasp Detection Using Convolutional Neural Networks

We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks. Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques. The model outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU. Our network can simultaneously perform classification so that in a single step it recognizes the object and finds a good grasp rectangle. A modification to this model predicts multiple grasps per object by using a locally constrained prediction mechanism. The locally constrained model performs significantly better, especially on objects that can be grasped in a variety of ways.Comment: Accepted to ICRA 201

Astronaut tool development: An orbital replaceable unit-portable handhold

A tool to be used during astronaut Extra-Vehicular Activity (EVA) replacement of spent or defective electrical/electronic component boxes is described. The generation of requirements and design philosophies are detailed, as well as specifics relating to mechanical development, interface verifications, testing, and astronaut feedback. Findings are presented in the form of: (1) a design which is universally applicable to spacecraft component replacement, and (2) guidelines that the designer of orbital replacement units might incorporate to enhance spacecraft on-orbit maintainability and EVA mission safety

A Lost Dream: Worker Control at Rath Packing

[Excerpted from Introduction by Gene Daniels] The story of Rath Packing Company of Waterloo, Iowa, is alternately a model of the American Dream and the story of a dream turned nightmare. Started in Iowa in 1891 with a work force of 22, Rath employed 8,000 people at its peak. In 1944, workers at Rath slaughtered 12,000 hogs, cattle and sheep a day. It was the largest and most modern packing house in the world. In the 1950s and early 1960s, however, Rath\u27s management failed to make several strategic moves. They failed to market Rath\u27s products to supermarkets, thinking Mom & Pop stores would remain the backbone of community grocery shopping. Little attention was paid to the growing conglomeration within the meatpacking industry itself And, management failed to re-invest in new machinery and processes and failed to build a new facility like the single-story buildings being constructed by competitors. All these factors combined to provide Rath with short-term prof its and long-term headaches. By the 1970s the company was in deep trouble

You Only Look Once: Unified, Real-Time Object Detection

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset

Improving Small Object Proposals for Company Logo Detection

Many modern approaches for object detection are two-staged pipelines. The first stage identifies regions of interest which are then classified in the second stage. Faster R-CNN is such an approach for object detection which combines both stages into a single pipeline. In this paper we apply Faster R-CNN to the task of company logo detection. Motivated by its weak performance on small object instances, we examine in detail both the proposal and the classification stage with respect to a wide range of object sizes. We investigate the influence of feature map resolution on the performance of those stages. Based on theoretical considerations, we introduce an improved scheme for generating anchor proposals and propose a modification to Faster R-CNN which leverages higher-resolution feature maps for small objects. We evaluate our approach on the FlickrLogos dataset improving the RPN performance from 0.52 to 0.71 (MABO) and the detection performance from 0.52 to 0.67 (mAP).Comment: 8 Pages, ICMR 201

Thermally isolated deployable shield for spacecraft

A thermally isolated deployable shield for spacecraft is provided utilizing a plurality of lattice panels stowable generally against the craft and deployable to some fixed distance from the craft. The lattice panels are formed from replaceable shield panels affixed to lattice structures. The lattice panels generally encircle the craft providing 360 degree coverage therearound. Actuation means are provided from translating the shield radially outward from the craft and thermally isolating the shield from the craft. The lattice panels are relatively flexible, allowing the shield to deploy to variable diameters while retaining uniform curvature thereof. Restraining means are provided for holding the shield relatively tight in its stowed configuration. Close-out assemblies provide light sealing and protection of the annular spaces between the deployed shield and the crafts end structure

Self-locking clamping tool with swivel jaws

A plier-like tool (11) having two plier-like members (13, 15) pivotally joined togther intermediate of their ends and having handle portions (17, 18) and swivel jaw members (29,30). An automatic locking mechanism (27) extending between the members permits an user to clamp the handle portions together so as to clamp the jaw members on an object (25) but holds the position so reached if the clamping action of the user is removed. A release device (65) is provided so that the jaw members may be opened up again. A compression spring (23) extending between the members (19, 20) assists in the opening of the jaw members. The swivel jaw members (29, 30) permit the user to rotate the plier-like members (13,15) relative to the object (25) being grasped