Efficient detectors for edge devices are often optimized for metrics like parameters or speed counts, which remain weak correlation with the energy of detectors. However, among vision applications of convolutional neural networks (CNNs), some, such as always-on surveillance cameras, are critical for energy constraints. This paper aims to serve as a baseline by designing detectors to reach tradeoffs between energy and performance from two perspectives: 1) We extensively analyze various CNNs to identify low-energy architectures, including the selection of activation functions, convolutions operators, and feature fusion structures on necks. These underappreciated details in past works seriously affect the energy consumption of detectors; 2) To break through the dilemmatic energy-performance problem, we propose a balanced detector driven by energy using discovered low-energy components named \textit{FemtoDet}. In addition to the novel construction, we further improve FemtoDet by considering convolutions and training strategy optimizations. Specifically, we develop a new instance boundary enhancement (IBE) module for convolution optimization to overcome the contradiction between the limited capacity of CNNs and detection tasks in diverse spatial representations, and propose a recursive warm-restart (RecWR) for optimizing training strategy to escape the sub-optimization of light-weight detectors, considering the data shift produced in popular augmentations. As a result, FemtoDet with only 68.77k parameters achieves a competitive score of 46.3 AP50 on PASCAL VOC and power of 7.83W on RTX 3090. Extensive experiments on COCO and TJU-DHD datasets indicate that the proposed method achieves competitive results in diverse scenes.

翻译：针对边缘设备的轻量级检测器通常以参数量或推理速度等指标作为优化目标，但这些指标与检测器的实际能耗关联较弱。然而，在卷积神经网络（CNN）的视觉应用中，诸如全天候监控摄像头等场景对能耗约束极其敏感。本文旨在通过从两个视角设计能量与性能权衡的检测器，建立该领域的基线：1）通过系统分析不同CNN架构，识别低能耗组件，包括激活函数、卷积算子以及颈部特征融合结构的选择。过往工作中这些被忽视的细节严重影响了检测器的能耗；2）为突破能量-性能两难困境，我们基于所发现的低能耗组件，提出一种能量驱动的平衡检测器FemtoDet。除创新性架构外，我们进一步通过卷积优化与训练策略改进提升性能：具体而言，针对CNN有限容量与检测任务中多样化空间表征之间的矛盾，开发了实例边界增强（IBE）模块用于卷积优化；考虑数据增强产生的分布偏移问题，提出递归热重启（RecWR）训练策略以帮助轻量检测器摆脱次优解。实验表明，FemtoDet仅以68.77k参数在PASCAL VOC上达到46.3 AP50的竞争性指标，在RTX 3090上功耗低至7.83W。在COCO与TJU-DHD数据集上的大量实验证实，本方法在多种场景下均取得具有竞争力的结果。