D\u00e9tecteur vid\u00e9o de v\u00e9hicules<\/u><\/a> dispose d'un algorithme sophistiqu\u00e9 de compensation des zones aveugles.<\/p> Au lieu de traiter la route comme un plan 2D plat, le syst\u00e8me d\u00e9finit des coordonn\u00e9es d'entr\u00e9e pr\u00e9cises (entr\u00e9e de la t\u00eate) et des coordonn\u00e9es de sortie (sortie de la queue) sur la section transversale de d\u00e9tection. En enregistrant ces horodatages transversaux, le syst\u00e8me d\u00e9duit math\u00e9matiquement si un v\u00e9hicule plus petit est arr\u00eat\u00e9 dans la zone obstru\u00e9e derri\u00e8re un camion.<\/p>
Cela porte la pr\u00e9cision de la longueur des files d'attente \u00e0 \u2265 90%, fournissant aux contr\u00f4leurs de feux de circulation les donn\u00e9es exactes n\u00e9cessaires pour optimiser les ondes vertes adaptatives et \u00e9viter les d\u00e9bordements localis\u00e9s. <\/p>
2. Classification avanc\u00e9e des d\u00e9tecteurs vid\u00e9o de v\u00e9hicules pour l'analyse du trafic urbain<\/strong><\/p>Plut\u00f4t que de regrouper les flux de trafic multimodaux dans un flux de donn\u00e9es unique et indiff\u00e9renci\u00e9, le d\u00e9tecteur vid\u00e9o de v\u00e9hicules ex\u00e9cute une classification en temps r\u00e9el \u00e0 travers une taxonomie urbaine hautement d\u00e9finie, isolant les voitures particuli\u00e8res, les poids lourds, les bus de transport en commun et les deux-roues. Simultan\u00e9ment, le processeur de p\u00e9riph\u00e9rie cartographie les analyses op\u00e9rationnelles critiques, g\u00e9n\u00e9rant des ensembles de donn\u00e9es continus pour l'occupation spatiale, l'occupation temporelle et l'avancement des v\u00e9hicules.<\/p>Caract\u00e9ristique\/m\u00e9trique<\/strong><\/strong><\/td>Capacit\u00e9 ST-VD5<\/td> Valeur pour les int\u00e9grateurs de syst\u00e8mes STI<\/td><\/tr> Distance de d\u00e9tection<\/strong><\/strong><\/td>80-100 m\u00e8tres (couvre jusqu'\u00e0 4 voies)<\/td> Moins d\u2019appareils n\u00e9cessaires par intersection ; des \u00e9conomies massives sur le mat\u00e9riel.<\/td><\/tr> Protocoles d'int\u00e9gration<\/strong><\/strong><\/td>GB28181, ONVIF, RTSP, GAT920<\/td> Se connecte de mani\u00e8re transparente aux plates-formes urbaines existantes sans probl\u00e8mes propri\u00e9taires.<\/td><\/tr> R\u00e9silience sur le terrain<\/strong><\/strong><\/td>\u00c9tanch\u00e9it\u00e9 IP66 et tol\u00e9rance de -40\u00b0C \u00e0 70\u00b0C<\/td> Op\u00e9rations sans entretien sous une chaleur torride ou des blizzards glacials.<\/td><\/tr><\/tbody><\/table><\/figure>3. Installation du d\u00e9tecteur vid\u00e9o de v\u00e9hicule et optimisation du d\u00e9ploiement sur le terrain<\/strong><\/p>Les logiciels haut de gamme perdent de leur valeur si un appareil met des heures \u00e0 s'aligner ou s'il d\u00e9rive facilement hors de sa position. Le d\u00e9tecteur vid\u00e9o de v\u00e9hicule pr\u00e9sente une conception de r\u00e9glage ind\u00e9pendant \u00e0 deux dimensions (rotation horizontale + inclinaison). Cette innovation m\u00e9canique permet aux ing\u00e9nieurs de terrain d\u2019aligner rapidement et pr\u00e9cis\u00e9ment les 3 sections transversales de d\u00e9tection de la cam\u00e9ra avec les lignes physiques des voies, \u00e9liminant ainsi compl\u00e8tement les angles morts des installations traditionnelles.<\/p>
4. Fusion radar-vid\u00e9o pour la d\u00e9tection du trafic par tous les temps<\/strong><\/p>Alors que les algorithmes Edge-AI et les technologies d'imagerie starlight ont consid\u00e9rablement am\u00e9lior\u00e9 les performances de d\u00e9tection optique du trafic, les syst\u00e8mes de d\u00e9tection du trafic bas\u00e9s sur la vision sont toujours confront\u00e9s \u00e0 des limites dans des conditions de visibilit\u00e9 extr\u00eamement faible telles que de fortes chutes de neige, un brouillard dense, des temp\u00eates de sable ou des environnements routiers mal \u00e9clair\u00e9s.<\/p>
Pour obtenir une d\u00e9tection de trafic plus stable par tous les temps sur des routes complexes \u00e0 plusieurs voies et des couloirs \u00e0 grande vitesse, l'industrie s'oriente de plus en plus vers des approches multicapteurs combinant l'analyse vid\u00e9o avec des technologies de d\u00e9tection de trafic radar.<\/p>
La fusion radar-vid\u00e9o permet aux syst\u00e8mes de d\u00e9tection de v\u00e9hicules de maintenir une continuit\u00e9 de suivi, une stabilit\u00e9 de d\u00e9tection et une collecte de donn\u00e9es de trafic plus fiables dans des conditions o\u00f9 la d\u00e9tection optique seule peut s'av\u00e9rer insuffisante.<\/p>
\u00c0 mesure que les syst\u00e8mes de transport intelligents continuent d\u2019\u00e9voluer, la d\u00e9tection du trafic multi-capteurs devrait devenir une direction importante pour la gestion intelligente du trafic de nouvelle g\u00e9n\u00e9ration.<\/p>
Au Sinowatcher Technology Co., Ltd.<\/a>, nous nous pr\u00e9parons \u00e9galement \u00e0 introduire une nouvelle solution de d\u00e9tection de v\u00e9hicules par fusion radar-vid\u00e9o qui int\u00e8gre la d\u00e9tection radar avec une analyse vid\u00e9o bas\u00e9e sur l'IA pour am\u00e9liorer les performances de d\u00e9tection du trafic dans des environnements r\u00e9els complexes. Des informations suppl\u00e9mentaires sur cette solution seront partag\u00e9es dans les futures mises \u00e0 jour.<\/p>","protected":false},"excerpt":{"rendered":" Un syst\u00e8me de d\u00e9tection de v\u00e9hicules ne peut pas simplement s\u2019appuyer sur des composants de s\u00e9curit\u00e9 standards. Il doit \u00e9voluer vers un hub d\u2019informatique de pointe hautement sp\u00e9cialis\u00e9. <\/p>","protected":false},"author":6,"featured_media":8862,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"footnotes":""},"categories":[38,44],"tags":[],"class_list":["post-8842","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-industry-news"],"acf":[],"yoast_head":"\n
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<\/figure><\/div>