MECAIL：14.6 KB专家网络让交通边缘设备做目标检测增量学习
MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts
交通摄像头的模型更新包只要14.6KB，在ODinW-13和D-RICO上成绩不输大参数方案。
智能交通系统要在停车场、加油站、渡轮、工地等特定时空场景持续改进目标检测模型，但边缘设备算力不足，更新通常由中心服务器下发。论文为此设定每个模块 14.6 KB 的硬上限，让更新能装进首个 TCP 窗口，并减少 UDP/BTP 在 V2X、Wi-Fi、2G-5G 链路上的分片。MECAIL（Mixture-of-Experts for Communication-Aware Incremental Learning）是首个满足该限制的方法，每个新场景由一个小型专家网络适配固定的基座模型。在 D-RICO 和 ODinW-13 两个基准上，它的成绩接近参数量远高于自己的方案，带宽开销也适合大规模部署。
MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts
Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However, most edge devices lack the computational resources to support on-device IL, requiring updates to be transmitted from centralized servers. We propose using this setup to obtain dense, specialized module coverage that adapts a fixed base model to specific spatiotemporal contexts, such as parking lots, gas stations, ferries, or construction sites. However, in order to reliably transmit these modules to the edge device, using TCP, UDP, and BTP over V2X, Wi-Fi, and 2G-5G hardware, we establish a strict limit of 14.6 KB per module to fit within the first TCP window and to minimize UDP/BTP fragmentation. We further introduce Mixture-of-Experts for Communication-Aware Incremental Learning (MECAIL), the first method that meets this strict requirement, in which each new domain or environment is served by a small expert network that adapts the base model. We validate MECAIL on D-RICO and ODinW-13, where it largely matches the performance of parameter-heavy approaches while enabling practical, bandwidth-efficient large-scale deployment. This allows comprehensive coverage by experts for highly specific, focused, and temporary situations.
