Aiming to address the challenges of arduous threshold delineation, inadequate robustness, and insufficient adaptability of conventional clearing loss detection sensors that depend on temporal domain feature thresholds to distinguish kernel impact signals, a comprehensive clearing loss detection system for corn kernel direct collectors was developed, and a kernel impact classification algorithm predicated on a variable scale one-dimensional convolutional neural network (VS-1D CNN) was proposed. Initially, the hardware circuitry and software processing program were engineered for impact signal acquisition, processing, and transmission, alongside the development of the supporting host computer. Subsequently, a data acquisition testing platform was established to gather and archive the impact signals of weeds and maize kernels under varying impact heights and angles, thereby constructing a data set and training the VS 1D CNN seed impact classification algorithm, with the training outcomes indicating that the model’s accuracy was 94.2% on the testing set. Ultimately, the efficacy of the devised detection system under diverse operational conditions and the classification performance of distinct stray residues and seed mixtures were validated, with results demonstrating that the proposed VS-1D CNN algorithm performed commendably, achieving detection accuracy exceeding 95% across different installation sites and varying seed flow rates;the classification accuracy for identifying different proportions of stray residues and seed mixtures surpassed 93% , signifying that the proposed algorithm exhibited exceptional performance. This underscored that the algorithm delineated in this manuscript possessed remarkable efficacy and can accurately detect seed losses without establishing a fixed temporal domain feature threshold.