To facilitate intelligent vision applications such as traffic surveillance and satellite monitoring, resource-constrained cameras are widely deployed in remote locations to capture and transmit images to edge servers for object detection. However, the network environment in Internet of Things scenarios experiences extremely constrained bandwidth and interference during long-distance transmission, leading to intolerable prolonged end-to-end latency and ineffective detection. Inspired by recently emerging generative vision models such as Masked AutoEncoder, we proposed GEN, an edge-assisted object detection system expedited by a novel image transmission scheme based on MAE. Specifically, GEN incorporates MAE by adopting patch-based image transmission. Only a small portion of image patches are required to transmit, while the complete image is reconstructed at the edge before detection. For transmitting each image patch, a blockwise patch packetization method aided by interpolation-based patch restoration is designed to cope with pixel loss incurred by interference. In order to prioritize the transmission of patches potentially containing more object features, a lightweight content-aware patch prioritization approach is adopted to ensure that enough object features are received before initiating image reconstruction. Evaluation results show that GEN can achieve \(2.6\times \) speedup while maintaining similar detection accuracy in bandwidth-constrained network environment compared with the existing edge-assisted object detection systems that adopt the common compression-based image transmission scheme.