When building or renovating a warehouse, a brainstorming phase is required to discuss robotic automation. Indeed, in order to achieve optimal performances, enhancements to the goods selection processes are continually sought. This selection uses important information based on moving products. Recently, several new methods have been emerged and the time to try them still limited. To evaluate the performance of these methods, it is necessary to carry out some tests. In this paper, we introduce a small-scale simulator designed to facilitate the testing of innovations outlined in the literature. Like a real warehouse, we have a conveyor belt to simulate the movement of goods and the robotic arm proposed by the Ned2. This research presents, with limited resources, the performance of a novel method in object detection. The simulation operates autonomously and is controlled by an NVIDIA Jetson Nano card, which incorporates novel deep-Learning methods. Furthermore, a depth camera is integrated to determine the 3D position of the goods.
Warehouses are a storage areas with high flow of goods. As part of the robotization of these areas, one of the major problems, attracting the attention of researchers, is defined by the automation of dispatching and renewing tasks when new products arrive. Indeed, having few information, automatic detection of these new products requires an update of the intelligent system, i.e. a new training step and therefore a stop in the warehouse production line. According to the literature, a novel branch of computer vision that consists in identifying and locating objects in an image with few information is called Low Shot Object Detection (LSOD). Using this solution, neural networks can automatically find new products with no need of any additional training step. To do so, neural networks architectures have been evolved by merging extracted features. This article presents a novel method that consists in merging layers convolution Siamese-ResNet network to include new products.
For many smart road applications, objects detection and recognition are one of the most important components. Indeed, precise detection of road objects is a critical task for autonomous urban driving and robotics technologies. In this paper, we describe our real-time smart system that consists in detecting and blurring undesirable road objects to anonymize and secure road users. Indeed, our proposed method is divided into three steps. The first step concerns the acquisition of images using the VIAPIX® system [1] developed by the ACTRIS company [2]. The second step is based on a neuronal approach for objects detection, namely vehicles, persons, road signs, etc. The third step allows to blur among the various objects detected only those which are undesirable on the road, i.e., person's faces, license plate. The obtained results demonstrate the efficiency of our robust approach in terms of good detection.
Behavioral analysis in an urban environment is a complex task that requires material and human resources, due to the difficulty of interpreting the situations. This paper presents a method to improve the detection of dangerous behaviors by assisting surveillance stations. Our objective is to alert when one of these behaviors is captured by a surveillance camera. To do this, we analyze the positions and paths of the persons in a global way, through a group of parameters. These parameters are determined by an automatic image analysis algorithm such as DBSCAN computed on an NVIDIA Jetson TX2. This analysis allows to detect, through the evolution and clustering of points in each cloud, phenomena qualified as abnormal, such as dispersion and rapid clustering, as well as poaching. The data used to feed our algorithm come from simulations that allow testing new and different scenarios. The performance of our proposed method is evaluated on videos representing real case situations.
In several particularly secure applications such as the entrance to a school, it is important to know whether the person entering is an adult or a child. In this article, we propose a human body morphology detector that distinguishes whether the person is an adult or a child. This detector could be included in a smart portal to detect whether the entry person is an adult or a child to apply a different treatment depending on the morphology. A person detector module1 is deployed to detect the presence of a person within a predefined radius. When the location of the person is detected, our system can measure the height of the person and determine if the person is an adult or a child based on its height.
Cities development accelerates with galloping urbanization on the surface on the world [1,2]. They must face significant threats linked to risks of human origin, like terrorism. In this paper, we present our approach for intrusion detection composed of 3 phases. The first one consists in selecting, via a GUI interface, zones supposed to be prohibited zones in an image. The second one, based on a Neural Network method, is applied for the person detection. The third one verifies if the detected person is present in one of the prohibited zones or not. If so, an alarm goes off automatically. Real tests were performed to secure an elementary school in the city of Nice in France. The obtained results showed the efficiency of our method in terms of good detection. Other work is in progress with the aim of deeply analyzing the intrusion to detect the abnormal behavior.
Road maintenance management presents a complex task for road authorities. The first presumption for the evaluation analysis and correct road construction rehabilitation is to have precise and up-to-date information about road pavement condition and level degradation. Different road crack types were proposed in the state of art in order to provide useful information for making pavement maintenance strategies. For this reason, we present in this paper a novel research to automatically detect and classify road cracks on two-dimensional digital images. Indeed, our proposed package is composed of two methods: crack detection and crack classification. The first method consists in detecting the cracks on images acquired by the VIAPIX® system developed by our company ACTRIS. To do so, we are based on our unsupervised approach cited in for road crack detection on two-dimensional pavement images. Then, in order to categorize each of the detected cracks, the second method of our package is applied. Based on principal component analysis (PCA), our method permits the classification of all the detected cracks into three types: vertical, horizontal, and oblique. The obtained results demonstrate the efficiency of our robust approaches in terms of good detection and classification on a variety of pavement images.
In this paper we present a novel approach for road sign identification and geolocation based on Joint Transform Correlator “JTC” and VIAPIX module. The proposed method is divided into three parts: identification, gathering and geolocation. The first part permits to detect and identify road signs on images acquired by the VIAPIX module [1] developed by our company ACTRIS [2]. To do so, we are based on our own method cited in [3] for road sign identification. The second part of our proposed approach consists in gathering the identified road sign by using the JTC technique [4]. Since the VIAPIX® module provides images at an interval of one image per meter, we identify each road sign by finding the number of images where this road sign has been recognized while computing thereby on each of these images its corresponding pixel coordinates. Finally, each road sign is geolocated using its pixel coordinates on several images. At this stage, we are based on the axial stereovision method [5]. Indeed, relying on the pixel coordinates and the distance between different images, we compute the 3D coordinates of each road sign. Thus, GPS coordinates can be then found using the GPS position of the vehicle basing on Vincenty formulae [6].
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