Vehicle object detection is a fundamental component of intelligent transportation systems, yet its deployment in resource-constrained environments is limited by the trade-off between accuracy and computational efficiency. This work systematically examines the relationship between network depth and detection performance through controlled experiments on both convolutional and attention-based architectures. The results reveal that accuracy does not scale indefinitely with depth, but instead exhibits diminishing returns and converges to a task-dependent performance plateau. A mutual information-based analysis attributes this saturation to fundamental limitations in information retention and representational efficiency. Motivated by this insight, we propose the TritonFocus Network (TF-Net), an architecture that integrates large-Kernel contextual perception with small-Kernel local aggregation for efficient feature representation. Extensive evaluations on the UA-DETRAC and BITVehicle benchmarks demonstrate that TF-Net achieves a state-of-the-art accuracy–efficiency trade-off among lightweight detectors. TF-Net achieves a relative detection accuracy improvement of approximately 5.8%, while maintaining high computational efficiency and approaching twice the inference speed of the baseline model. These findings validate the efficacy of saturation-aware architectural optimization and provide theoretical and practical guidance for deploying high-performance detection models in edge computing scenarios.