The present disclosure provides a method for recognizing a small target based on a deep learning network. The method comprises: determining, based on a collected image, spot defect information through a recognition model including a first feature determination layer, a second feature determination layer, and a spot defect determination layer, determining, based on the collected image, a first feature map, determining, based on the first feature map, a second feature map by fusing with positional encoding, determining, based on the second feature map, a third feature map through the second feature determination layer, and obtaining, based on the third feature map, positional information of the spot defect through a first determination layer, and determining, based on the third feature map, classification information of the spot defect through a second determination layer.

A process includes receiving a target quality value, receiving a measured quality value, and sending an inspection control instruction. The inspection control instruction is based at least in part on the target quality value and the measured quality value. The measured quality value is generated by a product inspector configured to inspect a sample before being sorted and may also include data generated by an output product inspector configured to inspect a sample after passing a sorting phase. The target quality value indicates a desired quality value of an output group of samples. The inspection control instruction controls the method of inspection performed by the product inspector. The measured quality values are received by a computing device, which in turn outputs the inspection control instruction to a product inspector.

A system for creating a wiring guide graphic comprising a database configured to receive inputs of a connector body identity, an engineering specification and a wiring table; and a processor coupled to the sensor, the processor configured to create a wiring guide graphic.

A soil-based coupon having an electronically readable verification code printed thereon is used for verification of soil removal by a cleaning process. The coupon includes a soil overlay covering the verification code. The verification code is at least partially revealed by removal of all or part of the soil overlay during the cleaning process. If the data encoded in the verification code can be correctly decoded after completion of the cleaning process, the cleaning process can be verified. The soil overlay can be designed to match the application. The coupons may be used in a cleaning process verification procedure in which one or more computing devices analyze images of the verification codes to determine whether or not cleaning processes can be verified.

Personal equipment detection may utilize pose-based detection. Input image data may be evaluated to detect persons in the image data. For detected persons, regions of the persons may be determined. Personal equipment may be detected for the detected persons in the image data and compared with the regions of the persons to determine whether the detected personal equipment is properly placed on the person.

Disclosed herein are systems and methods for efficiently and economically manufacturing molded polymer products such as those that require a two-shot, two-material injection molding process, with complex geometries and incorporate metal components such as inserts. The systems and methods include compact manufacturing cells and processes of operating such manufacturing cells. The manufacturing cells include a single injection molding machine with two molds arranged to simultaneously operate both molds. The manufacturing cell is arranged to be fully automated so that it is operative without the need for intervention or management from dedicated personnel. Such automation includes the automated sorting and placement of metal inserts into the injection molding machine, the automated removal of the product after the first molding stage and second molding stage, the automated inspection of every finished product, the automated sorting of conforming and rejected products, and the automated packaging of finished product for shipment to end user.

Image quality metrics are applied to the entire image within the field of view of a camera associated with a mobile device and are combined with non-image metrics applied to the device to provide a hybrid approach to capturing a quality image that is not dependent upon the check image satisfying a monitoring criterion. When the composite image passes image monitoring criteria and the device passes a non-image criterion, an image may be captured by the camera either manually or automatically. The process may be applied to capture images of both the front and the back of the check, and may be applied to multiple checks. The images may then be submitted for processing for deposit of the check or checks into the user's bank account without the requirement that the user review and approve the image or images of the back of the check or checks.

A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.

A computer-readable recording medium having stored therein an information processing program is provided. The information processing program causes a computer to execute processing including tracking an action taken by a user on an object on the basis of a captured image, distinguishing, on the basis of the action, between a first action in which the user holds the object and a second action in which the user releases the object held by the user, storing, on a first storage unit, first identification information and first location information with respect to the first action, storing, on a second storage unit, second identification information and second location information with respect to the second action, and in a case where the first action is detected at a position indicated in the second location information, storing the second identification information and the second location information on the first storage unit.

The disclosure is directed to, among other things, systems and methods for troubleshooting equipment installations using machine learning. Particularly, the systems and methods described herein may be used to validate an installation of one or more devices (which may be referred to as “customer premises equipment (CPE)” herein as well) at a given location, such as a customer's home or a commercial establishment. As one non-limiting example, the one or more devices may be associated with a fiber optical network, and may include a modem and/or an optical network terminal (ONT). However, the one or more devices may include any other types of devices associated with any other types of networks as well.